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INNOVATION AND GROWTH RATIONALE FOR AN INNOVATION STRATEGY ORGANISATION FOR ECONOMIC CO-OPE

INNOVATION AND GROWTH R ATIONALE FOR AN I NNOVATION S TRATEGYORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT The OECD is a unique forum where the governments of 30 democracies work together to address the economic, social and environmental challenges of globalisation. The OECD is also at the forefront of efforts to understand and to help governments respond to new developments and concerns, such as corporate governance, the information economy and the challenges of an ageing population. The Organisation provides a setting where governments can compare policy experiences, seek answers to common problems, identify good practice and work to co-ordinate domestic and international policies.The OECD member countries are: Australia, Austria, Belgium, Canada, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, Mexico, the Netherlands, New Zealand, Norway, Poland, Portugal, the Slovak Republic, Spain, Sweden, Switzerland, Turkey, the United Kingdom and the United States. The Commission of the European Communities takes part in the work of the OECD.OECD Publishing disseminates widely the results of the Organisation’s statistics gathering and research on economic, social and environmental issues, as well as the conventions, guidelines and standards agreed by its members.© OECD 2007No translation of this document may be made without written permission. Applications should be sent to rights@.INNOVATION AND GROWTH: RATIONALE FOR AN INNOVATION STRATEGY – 3© OECD 2007PrefaceUndoubtedly the capability to innovate and to bring innovation successfully tomarket will be a crucial determinant of the global competitiveness of nations overthe coming decade. There is growing awareness among policymakers that innovative activity is the main driver of economic progress and well-being as well as a potentialfactor in meeting global challenges in domains such as the environment and health. Not only has innovation moved to centre-stage in economic policy making, but there is a realisation that a co-ordinated, coherent, “whole-of-government” approach is required. Many OECD member countries have adopted national strategic road-maps to foster innovation and enhance its economic impact. Even countries that have generally refrained from active industrial policy in recent years now seek new ways to improve the environment for innovation in order to boost productivity and growth. The United States, for example, came forward with the “Innovate America” strategy in 2005. The EU’s “Lisbon Agenda”, initiated in 2000, has now been updated and strengthened.In addition to the rapid advances in scientific discovery and in general-purposetechnologies such as ICTs and biotechnology, the accelerating pace of innovation is being driven by globalisation. These pervasive trends were picked up at the summit of the G8 at Heiligendamm in June 2007 which identified research and innovation as areas requiring high-level policy dialogue between the G8 members and major emerging economies.A shorter version of this document was submitted to the meeting of the OECD Council at Ministerial level “Innovation, Growth and Equity” held in Paris in May 2007. It provided supporting evidence, based on the findings and recommendations emerging from recent OECD work, to underpin the Ministerial discussions on how policies should be updated to address the changing relationships between innovation and national progress. At that meeting, Ministers asked the OECD to develop a broad-ranging Innovation Strategy to build on existing work, address remaining knowledge gaps, and above all provide a cross-disciplinary mutually-reinforcing package of policy elements and recommendations to boost innovation performance.CHAPTER TITLE – 5© OECD 2007 Executive SummaryThe challengeToday, innovation performance is a crucial determinant of competitiveness and national progress. Moreover, innovation is important to help address global challenges, such as climate change and sustainable development. But despite the importance of innovation, many OECD countries face difficulties in strengthening performance in this area. Indeed, many OECD countries have seen little improve-ment in productivity performance in recent years despite the new opportunities offered by globalisation and new technologies, especially the information and communication technologies (ICT).A reform agendaGovernment policies can support innovation by continually reforming and updating the regulatory and institutional framework within which innovative activity takes place. In this context, reforms are needed to make public policy and regulatory framework more conducive to innovation in a range of policy areas from the general business environment — especially in the services, particularly in the network industries — to international trade and international investment, financial markets, labour markets, and education.Governments can also play a more direct role in fostering innovation. Public investment in science and basic research can play an important role in developing ICT and other general-purpose technologies and, hence, in enabling further innova-tion. This highlights the importance of reforming the management and funding of public investment in science and research, as well as public support to innovative activity in the private sector. The latter calls for an appropriate mix of direct and indirect instruments such as tax credits, direct support and well-designed public-private partnerships, support for innovative clusters and rigorous evaluation of such public support.In view of the changing environment for innovation, it is also important to consider whether the current system of IPR rules and practices continues to stimulate innovation while allowing access to knowledge. In certain cases the abuse of the control with which IPR owners are endowed could hamper competition, fair use and the diffusion of technology. However, regardless of issues related to the flexibility of the IPR system, stronger efforts are needed to combat counterfeiting and piracy, which are serious and growing problems.The need for political leadership and resolveImplementing reforms to foster innovation may prove difficult. Strong political leadership and efforts to develop a clear understanding by the various stakeholders of the problems and the solutions — including the costs they involve — can all help to communicate the need for reform and facilitate acceptance.6 – INNOVATION AND GROWTH: RATIONALE FOR AN INNOVATION STRATEGY © OECD 2007INNOVATION AND GROWTHPolicy issues and challenges The role of innovation for growth is strengthened by advances in new technologies, and a greater focus on knowledge creation and use ...Much of the rise in living standards is due to innovation — this has been the case since the Industrial Revolution. Today, innovative performance is a crucial factor in determining competitiveness and national progress. Moreover, innovation is important to help address global challenges, such as climate change and sustainable development.But it is the application of advances in technology, in conjunction with entre-preneurship and innovative approaches to the creation and delivery of goods and services, which translates scientific and technological advances into more productive economic activity. This results in economic growth if market structures and the regulatory environment enable the more productive activities to expand. This said, the innovative effort itself, including formal research and development, remains the sine qua non of growth.Evidence suggests that innovative effort is on the rise as a share of economic activity. Investment in knowledge has grown more rapidly than investment in machinery and equipment since the mid-1990s in most OECD countries, and has surpassed the latter in a few countries such as Finland and the United States (OECD, 2005c). R&D intensity of the economy has risen significantly in a number of — smaller — OECD countries, but remains more or less unchanged in the OECD area as a whole since 1995, and important cross-country differentials remain (Figure 1).But intellectual assets taken as a whole — a concept seeking to aggregate measures of human capital, R&D and capacity to conduct it, patent valuations as well as intangible assets such as brand value or firm-specific knowledge — are rapidly becoming the key to value creation through a number of channels. Improvements in the skill composition of labour play an important role in productivity growth. Studies suggest that investment in R&D is associated with high rates of return. And investments in software have also contributed significantly to business performance and economic growth, accounting for as much as one-third of the contribution of ICT (information and communications technology) capital to GDP growth since 1995 in Denmark, France, the Netherlands, Sweden and the United States (OECD, 2007a).INNOVATION AND GROWTH: RATIONALE FOR AN INNOVATION STRATEGY – 7© OECD 2007 Figure 1. Growth in R&D intensity (GERD1 as % of GDP), 1995-2005Source: OECD: Main Science and Technology Indicators database, December 2006.2005 data for some countries are the latest available....as well as globalisation and theintensifying economic challenge fromnon-OECD countries.More recently, the importance of innovation has been reinforced both by globalisation and by rapid advances in new technologies, notably ICTs, which have enabled new forms of competition and opened new markets for the creation and delivery of innovative products and services. Globalisation has also increased the pressure on OECD countries to move up the value chain and engage in a continuous process of adjustment and innovation.There has been a significant increase in R&D effort in a number of economies outside the OECD area (Figure 1), and, albeit starting from a low base, the associated growth of R&D capabilities in a number of major emerging market economies is making them competitive destinations for cross-border R&D. At least China among them is now a key global player in R&D in terms of absolute size as well as growth rates, with Gross Expenditure in R&D reaching USD115 billion in 2005 (at PPPs), compared to USD227 billion in the EU (provisional) or USD118 billion in Japan in 2005 (OECD, 2006c).8 – INNOVATION AND GROWTH: RATIONALE FOR AN INNOVATION STRATEGY © OECD 2007As a result, major emerging market economies are no longer simply low value-added producers but are adding their weight to the creation and commercialisation of innovative products, processes and services. Trade data on the four most significant economies (Brazil, Russia, India and China; “BRIC”) show that these have become more active in higher technology industries over the past decade. Figure 2 shows that between 1996 and 2004 the share of high technology goods has doubled to reach about 30 percent of total trade (exports plus imports) in manufactured goods by the BRIC countries. It should be noted that most of this rise is accounted for by China. Most of China’s exports of high-tech products is due to foreign firms, however, that use China as a location for some elements of their overall production network. When seen against the background of increasing focus and capabilities in innovation, expansion of R&D and rising human capital in BRIC countries, in particular China, this suggests that the challenge to OECD countries emanating from major emerging market economies is likely to intensify. At the same time, the emergence of these economies offers major opportunities for OECD countries, as these countries offer new markets for innovative products and provide access to a new supply of highly skilled workers.Figure 2. The changing structure of BRIC’s 1 manufacturing trade by technological intensity1. BRIC: Brazil, Russia, India and China.Source: OECD, Bilateral Trade Database.INNOVATION AND GROWTH: RATIONALE FOR AN INNOVATION STRATEGY – 9 © OECD 2007 At the same time, many OECD countries face difficulties in strengthening innovationWhile these developments make it even more urgent for OECD countries to move up the value chain, many of them face difficulties in strengthening innovation performance. For example, progress along these lines under the aegis of the Lisbon strategy of the European Union has been slow. Nevertheless, the most recent evidence suggests that the renewed Lisbon Strategy may have had some success in helping to improve the European Union's performance in innovation and R&D. Earlier OECD analysis (Sheehan & Wyckoff, 2003) has shown the economic and structural implications of policy initiatives to increase the R&D intensity of the economy, which is one of the key elements of the Lisbon strategy, and underlined the difficulty inherent in using R&D targeting as an indicator where success requires implementing policies across a broad range of domains, from R&D funding and entrepreneurship to immigration and education, to product, financial and labour market regulation.Indeed, recent OECD analysis has shown that increases in R&D intensity and innovation are driven by a wide range of factors (OECD, 2006b), including:∙ Reduction of anti-competitive product market regulations, which stimulatesbusiness R&D and strengthens the incentives to innovate. Moreover, a low level of restrictions on foreign direct investment is important, as it can improve cross-border knowledge transfers.∙ Stable macroeconomic conditions and low real interest rates which encouragethe growth of innovation activity by creating a stable and low-cost environ-ment for investment in innovation.∙ Availability of internal and external finance.∙ An expansion in public research, which can support business sector research,although expanding both at the same time will require efforts to raise the supply of human resources.∙ Fiscal incentives, which can be effective in raising R&D, especially when firms face financial constraints. Tax relief for private R&D is often found to provide astronger stimulus to business R&D than direct government support. This may be because much direct support for R&D is aimed at meeting government objectives, such as energy security or defence, and not at stimulating private R&D.∙ Openness to foreign R&D, which is associated with higher productivity growth,especially when domestic R&D investment and capabilities are also high.10 – INNOVATION AND GROWTH: RATIONALE FOR AN INNOVATION STRATEGY © OECD 2007 Indeed, the last few years have seen an increasing public policy focus on what promotes greater innovation. Nevertheless, many OECD countries have seen little improvement in productivity performance in recent years despite the new opportunities offered by globalisation and by new technologies, especially ICT. Figure 3 depicts the wide dispersion among OECD countries in terms of multi-factor productivity (MFP) growth over the past decade. Indeed, MFP growth rates have declined in many countries, including the larger Continental European economies.Figure 3. Multi-factor productivity growth, 1995-2000 and 2000-2005Note: Owing to data availability constraints, exact dates for the most recent period differ for some countries. Source: OECD Productivity Database. Intellectual property rights [IPR] pose a particularly important challengeAs knowledge has become an increasingly essential factor of growth and competitiveness, for companies as for nations, its market value has increased, hence reinforcing the value of protection for creators. There have also been concomitant changes in the economic context which tended to weaken the effectiveness of the IPR system as it operated previously:∙ New technologies, initially not covered by patent systems, have emerged, notably in the fields of software and biotechnology.∙ Globalisation has made imitation and counterfeiting both more rewarding (in an expanded market) and more feasible, as a number of countries registered significant growth in technological capabilities, without a corresponding development in their IPR system.∙ ICTs, and notably the Internet, have made copying of creative contents easier.Governments have reacted to these changes by strengthening the rights of IPR holders. IPR laws and practice have evolved in accordance with this objective inmany countries. The patent subject matter has been extended to genetic materialand, with restrictions in certain jurisdictions, to software inventions. Many countrieshave set up a central court for addressing IPR cases in a more harmonised andeffective way. Copyright protection has been lengthened (70 years after death of theauthor in Europe in 1993 and in the US in 1998) and extended to creations in digitalform1. Last but not least, international standards for protection, notably in developingcountries, have been raised through the TRIPs Agreement (1994).In reaction to these changing conditions, and in view of ensuring a high return on their investment in creations, firms are increasingly applying for IPR. The number ofpatent applications and trademark applications, for instance, has increased markedlyover the past two decades (Figures 4 and 5).Figure 4. Number of patent applications (EPO, JPO, USPTO) by filing dateIndex 1991 = 1001. The US Digital Millennium Copyright Act (DMCA) of 1998, European enforcement directive of 2006. Databaseshave been the object of an ad-hoc IP law in the EU (1999).© OECD 2007Figure 5. Number of trademark applications (US, Japan, Germany)Index 1991 = 100Note: Data for Japan are adjusted to take account of a break in the series in 1997.Source: WIPO, Statistics on Trademarks, Online data, www.wipo.int/ipstats/en/statistics/marks/The key policy question remains how to strike an appropriate balance between providing incentives and rewards to innovators and providing access to newknowledge for users. Over the recent past the balance has been shifting more infavour of right holders, at least partly in reaction to changing conditions but also as adeliberate move towards “pro-IPR policies”. These policies have resulted in certainachievements, such as the progress of technology transfers from universities incountries which have promoted patenting of public research results; the expansionof the biotech sector, which would have been hardly possible without patents ongenetic inventions; and the multiplication of venture capital based start ups, whichoften rely on IPR.As the policy, legal and economic environment are still evolving, the situation in the field of IPR is not stabilised. Public debates have turned around the efficiencyand distributive effects of consolidating IPR regimes. If strong IPR are needed inorder to give incentives for creative activities, they should not on the other handendow the holder with such broad rights as to block all access to new knowledge.Difficulties have notably emerged in the following areas:© OECD 2007∙Access to inventions for research use (biotech) or for further improvement or adaptation (software) have reportedly been hampered by patents in a numberof cases, hence slowing down research.∙Establishment of standards for interoperability and other collective use of IPR have sometimes been delayed or made more costly by opportunistic strategiesbased on IPR.∙The backlog at most patent offices in the world has exploded (over 5 years pendency time in a number of cases), creating legal uncertainty on a vast scale,while there have been concerns about the quality of patents awarded.∙The extension of digital rights management (DRM) systems is meeting with resistance as they put strict restrictions on the rights of users, hence reducingde facto the scope of “fair use” of copyright law, for instance.∙No satisfactory formula has yet been found for ensuring that creators are rewarded while fully using the fluidity offered by the Internet.Addressing the policy challengesProduct and labour market reform wouldpromote innovation …Policy reforms are needed to strengthen innovation and productivity outcomes.Improving the business environment for innovation is especially important, as business is the main driver of innovation. Further liberalisation of the services sector and of network industries could foster stronger innovation in these sectors, which account for over 70% of GDP in OECD economies. More innovation-friendly regulation, combined with lower barriers to trade and foreign direct investment would enhance competition and would foster the flow of technology and knowledge across borders. Reform of labour markets, notably through well-designed employ-ment protection legislation, would help firms to adjust and allow them to draw greater benefits from their investment in innovation and technology.OECD studies have established a broad negative link between the restrictiveness of economic regulations in product and labour markets, and productivity growth.2 In the product markets limited competition among suppliers may increase the cost of inputs and make products supplied less innovative. It may discourage innovation, or make it costly to develop it or to defend the associated intellectual property. In turn, restrictive labour markets may curtail firms’ ability to put in place the changes in the workforce and firm organisation necessary to reap benefits from new technology deployed.More specifically, OECD empirical analysis shows that competition-restraining regulations slow the rate of catch-up with the technological frontier, where labour productivity is the highest. By implication, countries could have achieved signifi-cantly faster productivity growth over the 1995-2003 period if they had aligned their regulations in each non-manufacturing sector on the least constraining stance in the OECD area in that industry (Figure 6) (OECD 2007c).2. See OECD (2007) Going for Growth 2007, Chapter 5 for a recent update of evidence.© OECD 2007Figure 6. Potential increase of annual business sector productivity growth over the period 1995 to 2003 if regulatory stance least restrictive of competition had been adopted11. Data are the average increase in annual business-sector productivity over the period 1995 to 2003 given an easing in the stance of regulation to the least restrictive of competition in the non-manufacturing sectors in OECD countries in 1995. The business-sector results are calculated as weighted averages of the sectoral productivity increases using value-added weights.Source: Conway, P. et al. (2006).In particular, investment in ICTs is positively correlated with uptake and diffusion of innovation (OECD 2004b). The use of ICT is closely linked to the abilityof firms to innovate, i.e. introduce new products, services, business processes andapplications. Moreover, ICT has helped facilitate the innovation process, for exampleby speeding up scientific discovery. ICT has also fostered networking, which hasenabled informal learning and co-operation between firms, as well as outsourcing.But there is a large variation of ICT investment as a share of total investment inOECD countries — it is substantially higher in relatively lightly regulated economies,such as Australia, Finland, Sweden, the United Kingdom and the United States, thanin Continental European economies (Figure 7).Other studies suggest that specific policy breakthroughs removing anti-competitive regulations, such as those associated with the unbundling of ICTsoftware from hardware, break up of telecommunications monopolies, restriction ofentry in parcel delivery or air transportation sectors, have often spurred majorwaves of innovation (OECD, 2005b).That said, an innovation- and growth-friendly regulatory environment does not entail across-the-board de-regulation. Establishing appropriate regulation can be akey component of ensuring adequate competition and innovation in newly liberalisedmarkets and in markets where technological convergence requires an update of the© OECD 2007© OECD 2007regulatory framework (such as in telecommunications and broadcasting). Forth-coming work in conjunction with the OECD Ministerial Meeting on the future of the Internet economy 3 will develop policy options for appropriate regulatory reform to maintain the future Internet as a trusted, secure and reliable medium underpinning social and economic development.Figure 7. ICT investment as share of total investment, 2000-2005** Average of 2000-2005 (or latest year)51015202530I r e l a n d G r e e c e N o r w a y S p a i n P o r t u g a l A u s t r i a J a p a n I t a l y G e r m a n y N e t h e r l a n d s F r a n c e K o r e a D e n m a r k C a n a d a N e w Z e a l a n d B e l g i u m U n i t e d K i n g d o m A u s t r i a F i n l a n d S w e d e n U n i t e d S t a t e sSource : OECD Productivity database.Reform of financial markets can also boost innovation and growth, including by helping to reduce the financing gaps faced by some innovative small firms. Empirical literature suggests that industrial sectors that are most dependent on external finance tend to grow faster in countries that have better developed financial systems. Furthermore, the sectors that tend to be the most dependent on external financial sources are generally the ones that invest the most in R&D (e.g. pharmaceuticals, electronic equipment and refined petroleum products).43. Seoul, Korea, 17-18 June 2008, /FutureInternet .4.See OECD (2006b) Chapter 3 and in particular de Serres et al . (2006).The market for high-risk capital, in particular venture capital and less formal sources of finance such as business angels’ funds play a key role in the financing ofinnovation. Venture capital investment is relatively small in most European countriesand Japan as compared with North America, the United Kingdom and the Netherlands(Figure 8).Investment in innovation would be encouraged by deeper and more efficient venture capital markets and easier access to external finance. Differences in theavailability and/or use of venture capital across countries may to some extent berooted in different cultural attitudes towards entrepreneurship and risk taking, butthey also reflect policies that discourage risk-taking and the supply of risk capital.Improving disclosure of intellectual assets could also help improve the allocation of capital. Competition in financial markets already encourages companies toimprove their reporting and managerial practices on intellectual assets. However,best practices have not been widely disseminated across companies and juris-dictions, and governments could encourage the diffusion of best practices, alreadypioneered by advanced firms, in a principles-based manner (OECD, 2007a).Figure 8. Venture capital investment flows as a percentage of GDP,2005 or latest available year1. 2001 data.2. 2002 data.Source: OECD, Venture capital database. Quoted in OECD (2007d) Science, Technology and Industry Scoreboard 2007, forthcoming.© OECD 2007Figure 9. Enhancements in human capital contributing to labour productivity growth(1990-2000)Trend growth in GDP per person employedContribution to growth in GDP per person employed from changes in:1Hourly GDP per efficient unit of labourHours workedHuman capital1. Based on the following decomposition: growth in GDP per person employed = (changes in hourly GDP per efficient unit of labour) + (changes in average hours worked) + (changes in human capital).2. Year of reference 1990-1999.3. Mainland only.4. Year of reference 1991-2000.Note that the negative contribution of human capital to MFP growth in the Netherlands is a statistical artefact, due to a large fall in the rate of unemployment, and reintegration into employment of a large population with relatively lower skills, during the period under consideration.Source: see OECD (2005a) Education at a Glance, Chart A.10.2, p.150.© OECD 2007。

Universities in Evolutionary Systems(系统变革中的大学)

Universities in Evolutionary Systems of InnovationMarianne van der Steen and Jurgen EndersThis paper criticizes the current narrow view on the role of universities in knowledge-based economies.We propose to extend the current policy framework of universities in national innovation systems(NIS)to a more dynamic one,based on evolutionary economic principles. The main reason is that this dynamic viewﬁts better with the practice of innovation processes. We contribute on ontological and methodological levels to the literature and policy discussions on the effectiveness of university-industry knowledge transfer and the third mission of uni-versities.We conclude with a discussion of the policy implications for the main stakeholders.1.IntroductionU niversities have always played a major role in the economic and cultural devel-opment of countries.However,their role and expected contribution has changed sub-stantially over the years.Whereas,since1945, universities in Europe were expected to con-tribute to‘basic’research,which could be freely used by society,in recent decades they are expected to contribute more substantially and directly to the competitiveness ofﬁrms and societies(Jaffe,2008).Examples are the Bayh–Dole Act(1982)in the United States and in Europe the Lisbon Agenda(2000–2010) which marked an era of a changing and more substantial role for universities.However,it seems that this‘new’role of universities is a sort of universal given one(ex post),instead of an ex ante changing one in a dynamic institutional environment.Many uni-versities are expected nowadays to stimulate a limited number of knowledge transfer activi-ties such as university spin-offs and university patenting and licensing to demonstrate that they are actively engaged in knowledge trans-fer.It is questioned in the literature if this one-size-ﬁts-all approach improves the usefulness and the applicability of university knowledge in industry and society as a whole(e.g.,Litan et al.,2007).Moreover,the various national or regional economic systems have idiosyncratic charac-teristics that in principle pose different(chang-ing)demands towards universities.Instead of assuming that there is only one‘optimal’gov-ernance mode for universities,there may bemultiple ways of organizing the role of univer-sities in innovation processes.In addition,we assume that this can change over time.Recently,more attention in the literature hasfocused on diversity across technologies(e.g.,King,2004;Malerba,2005;Dosi et al.,2006;V an der Steen et al.,2008)and diversity offormal and informal knowledge interactionsbetween universities and industry(e.g.,Cohenet al.,1998).So far,there has been less atten-tion paid to the dynamics of the changing roleof universities in economic systems:how dothe roles of universities vary over time andwhy?Therefore,this article focuses on the onto-logical premises of the functioning of univer-sities in innovation systems from a dynamic,evolutionary perspective.In order to do so,we analyse the role of universities from theperspective of an evolutionary system ofinnovation to understand the embeddednessof universities in a dynamic(national)systemof science and innovation.The article is structured as follows.InSection2we describe the changing role ofuniversities from the static perspective of anational innovation system(NIS),whereasSection3analyses the dynamic perspective ofuniversities based on evolutionary principles.Based on this evolutionary perspective,Section4introduces the characteristics of a LearningUniversity in a dynamic innovation system,summarizing an alternative perception to thestatic view of universities in dynamic economicsystems in Section5.Finally,the concludingVolume17Number42008doi:10.1111/j.1467-8691.2008.00496.x©2008The AuthorsJournal compilation©2008Blackwell Publishingsection discusses policy recommendations for more effective policy instruments from our dynamic perspective.2.Static View of Universities in NIS 2.1The Emergence of the Role of Universities in NISFirst we start with a discussion of the literature and policy reports on national innovation system(NIS).The literature on national inno-vation systems(NIS)is a relatively new and rapidly growingﬁeld of research and widely used by policy-makers worldwide(Fagerberg, 2003;Balzat&Hanusch,2004;Sharif,2006). The NIS approach was initiated in the late 1980s by Freeman(1987),Dosi et al.(1988)and Lundvall(1992)and followed by Nelson (1993),Edquist(1997),and many others.Balzat and Hanusch(2004,p.196)describe a NIS as‘a historically grown subsystem of the national economy in which various organizations and institutions interact with and inﬂuence one another in the carrying out of innovative activity’.It is about a systemic approach to innovation,in which the interaction between technology,institutions and organizations is central.With the introduction of the notion of a national innovation system,universities were formally on the agenda of many innovation policymakers worldwide.Clearly,the NIS demonstrated that universities and their interactions with industry matter for innova-tion processes in economic systems.Indeed, since a decade most governments acknowl-edge that interactions between university and industry add to better utilization of scienti-ﬁc knowledge and herewith increase the innovation performance of nations.One of the central notions of the innovation system approach is that universities play an impor-tant role in the development of commercial useful knowledge(Edquist,1997;Sharif, 2006).This contrasts with the linear model innovation that dominated the thinking of science and industry policy makers during the last century.The linear innovation model perceives innovation as an industry activity that‘only’utilizes fundamental scientiﬁc knowledge of universities as an input factor for their innovative activities.The emergence of the non-linear approach led to a renewed vision on the role–and expectations–of universities in society. Some authors have referred to a new social contract between science and society(e.g., Neave,2000).The Triple Helix(e.g.,Etzkowitz &Leydesdorff,1997)and the innovation system approach(e.g.,Lundvall,1988)and more recently,the model of Open Innovation (Chesbrough,2003)demonstrated that innova-tion in a knowledge-based economy is an inter-active process involving many different innovation actors that interact in a system of overlapping organizationalﬁelds(science, technology,government)with many interfaces.2.2Static Policy View of Universities in NIS Since the late1990s,the new role of universi-ties in NIS thinking emerged in a growing number of policy studies(e.g.,OECD,1999, 2002;European Commission,2000).The con-tributions of the NIS literature had a large impact on policy makers’perception of the role of universities in the national innovation performance(e.g.,European Commission, 2006).The NIS approach gradually replaced linear thinking about innovation by a more holistic system perspective on innovations, focusing on the interdependencies among the various agents,organizations and institutions. NIS thinking led to a structurally different view of how governments can stimulate the innovation performance of a country.The OECD report of the national innovation system (OECD,1999)clearly incorporated these new economic principles of innovation system theory.This report emphasized this new role and interfaces of universities in knowledge-based economies.This created a new policy rationale and new awareness for technology transfer policy in many countries.The NIS report(1999)was followed by more attention for the diversity of technology transfer mecha-nisms employed in university-industry rela-tions(OECD,2002)and the(need for new) emerging governance structures for the‘third mission’of universities in society,i.e.,patent-ing,licensing and spin-offs,of public research organizations(OECD,2003).The various policy studies have in common that they try to describe and compare the most important institutions,organizations, activities and interactions of public and private actors that take part in or inﬂuence the innovation performance of a country.Figure1 provides an illustration.Theﬁgure demon-strates the major building blocks of a NIS in a practical policy setting.It includesﬁrms,uni-versities and other public research organiza-tions(PROs)involved in(higher)education and training,science and technology.These organizations embody the science and tech-nology capabilities and knowledge fund of a country.The interaction is represented by the arrows which refer to interactive learn-ing and diffusion of knowledge(Lundvall,Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing1992).1The building block ‘Demand’refers to the level and quality of demand that can be a pull factor for ﬁrms to innovate.Finally,insti-tutions are represented in the building blocks ‘Framework conditions’and ‘Infrastructure’,including various laws,policies and regula-tions related to science,technology and entre-preneurship.It includes a very broad array of policy issues from intellectual property rights laws to ﬁscal instruments that stimulate labour mobility between universities and ﬁrms.The ﬁgure demonstrates that,in order to improve the innovation performance of a country,the NIS as a whole should be conducive for innovative activities in acountry.Since the late 1990s,the conceptual framework as represented in Figure 1serves as a dominant design for many comparative studies of national innovation systems (Polt et al.,2001;OECD,2002).The typical policy benchmark exercise is to compare a number of innovation indicators related to the role of university-industry interactions.Effective performance of universities in the NIS is judged on a number of standardized indica-tors such as the number of spin-offs,patents and licensing.Policy has especially focused on ‘getting the incentives right’to create a generic,good innovative enhancing context for ﬁrms.Moreover,policy has also inﬂu-enced the use of speciﬁc ‘formal’transfer mechanisms,such as university patents and university spin-offs,to facilitate this collabo-ration.In this way best practice policies are identiﬁed and policy recommendations are derived:the so-called one-size-ﬁts-all-approach.The focus is on determining the ingredients of an efﬁcient benchmark NIS,downplaying institutional diversity and1These organizations that interact with each other sometimes co-operate and sometimes compete with each other.For instance,ﬁrms sometimes co-operate in certain pre-competitive research projects but can be competitors as well.This is often the case as well withuniversities.Figure 1.The Benchmark NIS Model Source :Bemer et al.(2001).Volume 17Number 42008©2008The AuthorsJournal compilation ©2008Blackwell Publishingvariety in the roles of universities in enhanc-ing innovation performance.The theoretical contributions to the NIS lit-erature have outlined the importance of insti-tutions and institutional change.However,a further theoretical development of the ele-ments of NIS is necessary in order to be useful for policy makers;they need better systemic NIS benchmarks,taking systematically into account the variety of‘national idiosyncrasies’. Edquist(1997)argues that most NIS contribu-tions are more focused onﬁrms and technol-ogy,sometimes reducing the analysis of the (national)institutions to a left-over category (Geels,2005).Following Hodgson(2000), Nelson(2002),Malerba(2005)and Groenewe-gen and V an der Steen(2006),more attention should be paid to the institutional idiosyncra-sies of the various systems and their evolution over time.This creates variety and evolving demands towards universities over time where the functioning of universities and their interactions with the other part of the NIS do evolve as well.We suggest to conceptualize the dynamics of innovation systems from an evolutionary perspective in order to develop a more subtle and dynamic vision on the role of universities in innovation systems.We emphasize our focus on‘evolutionary systems’instead of national innovation systems because for many universities,in particular some science-based disciplinaryﬁelds such as biotechnology and nanotechnology,the national institutional environment is less relevant than the institu-tional and technical characteristics of the technological regimes,which is in fact a‘sub-system’of the national innovation system.3.Evolutionary Systems of Innovation as an Alternative Concept3.1Evolutionary Theory on Economic Change and InnovationCharles Darwin’s The Origin of Species(1859)is the foundation of modern thinking about change and evolution(Luria et al.,1981,pp. 584–7;Gould,1987).Darwin’s theory of natural selection has had the most important consequences for our perception of change. His view of evolution refers to a continuous and gradual adaptation of species to changes in the environment.The idea of‘survival of theﬁttest’means that the most adaptive organisms in a population will survive.This occurs through a process of‘natural selection’in which the most adaptive‘species’(organ-isms)will survive.This is a gradual process taking place in a relatively stable environment, working slowly over long periods of time necessary for the distinctive characteristics of species to show their superiority in the‘sur-vival contest’.Based on Darwin,evolutionary biology identiﬁes three levels of aggregation.These three levels are the unit of variation,unit of selection and unit of evolution.The unit of varia-tion concerns the entity which contains the genetic information and which mutates fol-lowing speciﬁc rules,namely the genes.Genes contain the hereditary information which is preserved in the DNA.This does not alter sig-niﬁcantly throughout the reproductive life-time of an organism.Genes are passed on from an organism to its successors.The gene pool,i.e.,the total stock of genetic structures of a species,only changes in the reproduction process as individuals die and are born.Par-ticular genes contribute to distinctive charac-teristics and behaviour of species which are more or less conducive to survival.The gene pool constitutes the mechanism to transmit the characteristics of surviving organisms from one generation to the next.The unit of selection is the expression of those genes in the entities which live and die as individual specimens,namely(individual) organisms.These organisms,in their turn,are subjected to a process of natural selection in the environment.‘Fit’organisms endowed with a relatively‘successful’gene pool,are more likely to pass them on to their progeny.As genes contain information to form and program the organisms,it can be expected that in a stable environment genes aiding survival will tend to become more prominent in succeeding genera-tions.‘Natural selection’,thus,is a gradual process selecting the‘ﬁttest’organisms. Finally,there is the unit of evolution,or that which changes over time as the gene pool changes,namely populations.Natural selec-tion produces changes at the level of the population by‘trimming’the set of genetic structures in a population.We would like to point out two central principles of Darwinian evolution.First,its profound indeterminacy since the process of development,for instance the development of DNA,is dominated by time at which highly improbable events happen (Boulding,1991,p.12).Secondly,the process of natural selection eliminates poorly adapted variants in a compulsory manner,since indi-viduals who are‘unﬁt’are supposed to have no way of escaping the consequences of selection.22We acknowledge that within evolutionary think-ing,the theory of Jean Baptiste Lamarck,which acknowledges in essence that acquired characteris-tics can be transmitted(instead of hereditaryVolume17Number42008©2008The AuthorsJournal compilation©2008Blackwell PublishingThese three levels of aggregation express the differences between ‘what is changing’(genes),‘what is being selected’(organisms),and ‘what changes over time’(populations)in an evolutionary process (Luria et al.,1981,p.625).According to Nelson (see for instance Nelson,1995):‘Technical change is clearly an evolutionary process;the innovation generator keeps on producing entities superior to those earlier in existence,and adjustment forces work slowly’.Technological change and innovation processes are thus ‘evolutionary’because of its characteristics of non-optimality and of an open-ended and path-dependent process.Nelson and Winter (1982)introduced the idea of technical change as an evolutionary process in capitalist economies.Routines in ﬁrms function as the relatively durable ‘genes’.Economic competition leads to the selection of certain ‘successful’routines and these can be transferred to other ﬁrms by imitation,through buy-outs,training,labour mobility,and so on.Innovation processes involving interactions between universities and industry are central in the NIS approach.Therefore,it seems logical that evolutionary theory would be useful to grasp the role of universities in innovation pro-cesses within the NIS framework.3.2Evolutionary Underpinnings of Innovation SystemsBased on the central evolutionary notions as discussed above,we discuss in this section how the existing NIS approaches have already incor-porated notions in their NIS frameworks.Moreover,we investigate to what extent these notions can be better incorporated in an evolu-tionary innovation system to improve our understanding of universities in dynamic inno-vation processes.We focus on non-optimality,novelty,the anti-reductionist methodology,gradualism and the evolutionary metaphor.Non-optimality (and Bounded Rationality)Based on institutional diversity,the notion of optimality is absent in most NIS approaches.We cannot deﬁne an optimal system of innovation because evolutionary learning pro-cesses are important in such systems and thus are subject to continuous change.The system never achieves an equilibrium since the evolu-tionary processes are open-ended and path dependent.In Nelson’s work (e.g.,1993,1995)he has emphasized the presence of contingent out-comes of innovation processes and thus of NIS:‘At any time,there are feasible entities not present in the prevailing system that have a chance of being introduced’.This continuing existence of feasible alternative developments means that the system never reaches a state of equilibrium or ﬁnality.The process always remains dynamic and never reaches an optimum.Nelson argues further that diversity exists because technical change is an open-ended multi-path process where no best solu-tion to a technical problem can be identiﬁed ex post .As a consequence technical change can be seen as a very wasteful process in capitalist economies with many duplications and dead-ends.Institutional variety is closely linked to non-optimality.In other words,we cannot deﬁne the optimal innovation system because the evolutionary learning processes that take place in a particular system make it subject to continuous change.Therefore,comparisons between an existing system and an ideal system are not possible.Hence,in the absence of any notion of optimality,a method of comparing existing systems is necessary.According to Edquist (1997),comparisons between systems were more explicit and systematic than they had been using the NIS approaches.Novelty:Innovations CentralNovelty is already a central notion in the current NIS approaches.Learning is inter-preted in a broad way.Technological innova-tions are deﬁned as combining existing knowledge in new ways or producing new knowledge (generation),and transforming this into economically signiﬁcant products and processes (absorption).Learning is the most important process behind technological inno-vations.Learning can be formal in the form of education and searching through research and development.However,in many cases,innovations are the consequence of several kinds of learning processes involving many different kinds of economic agents.According to Lundvall (1992,p.9):‘those activities involve learning-by-doing,increasing the efﬁciency of production operations,learning-characteristics as in the theory of Darwin),is acknowledged to ﬁt better with socio-economic processes of technical change and innovation (e.g.,Nelson &Winter,1982;Hodgson,2000).Therefore,our theory is based on Lamarckian evolutionary theory.However,for the purpose of this article,we will not discuss the differences between these theo-ries at greater length and limit our analysis to the fundamental evolutionary building blocks that are present in both theories.Volume 17Number 42008©2008The AuthorsJournal compilation ©2008Blackwell Publishingby-using,increasing the efﬁciency of the use of complex systems,and learning-by-interacting, involving users and producers in an interac-tion resulting in product innovations’.In this sense,learning is part of daily routines and activities in an economy.In his Learning Economy concept,Lundvall makes learning more explicit,emphasizing further that ‘knowledge is assumed as the most funda-mental resource and learning the most impor-tant process’(1992,p.10).Anti-reductionist Approach:Systems and Subsystems of InnovationSo far,NIS approaches are not yet clear and systematic in their analysis of the dynamics and change in innovation systems.Lundvall’s (1992)distinction between subsystem and system level based on the work of Boulding implicitly incorporates both the actor(who can undertake innovative activities)as well as the structure(institutional selection environment) in innovation processes of a nation.Moreover, most NIS approaches acknowledge that within the national system,there are different institu-tional subsystems(e.g.,sectors,regions)that all inﬂuence each other again in processes of change.However,an explicit analysis of the structured environment is still missing (Edquist,1997).In accordance with the basic principles of evolutionary theory as discussed in Section 3.1,institutional evolutionary theory has developed a very explicit systemic methodol-ogy to investigate the continuous interaction of actors and institutional structures in the evolution of economic systems.The so-called ‘methodological interactionism’can be per-ceived as a methodology that combines a structural perspective and an actor approach to understand processes of economic evolu-tion.Whereas the structural perspective emphasizes the existence of independent institutional layers and processes which deter-mine individual actions,the actor approach emphasizes the free will of individuals.The latter has been referred to as methodological individualism,as we have seen in neo-classical approaches.Methodological indi-vidualism will explain phenomena in terms of the rational individual(showingﬁxed prefer-ences and having one rational response to any fully speciﬁed decision problem(Hodgson, 2000)).The interactionist approach recognizes a level of analysis above the individual orﬁrm level.NIS approaches recognize that national differences exist in terms of national institu-tions,socio-economic factors,industries and networks,and so on.So,an explicit methodological interactionist approach,explicitly recognizing various insti-tutional layers in the system and subsystem in interaction with the learning agents,can improve our understanding of the evolution of innovation.Gradualism:Learning Processes andPath-DependencyPath-dependency in biology can be translated in an economic context in the form of(some-times very large)time lags between a technical invention,its transformation into an economic innovation,and the widespread diffusion. Clearly,in many of the empirical case studies of NIS,the historical dimension has been stressed.For instance,in the study of Denmark and Sweden,it has been shown that the natural resource base(for Denmark fertile land,and for Sweden minerals)and economic history,from the period of the Industrial Revolution onwards,has strongly inﬂuenced present specialization patterns(Edquist& Lundvall,1993,pp.269–82).Hence,history matters in processes of inno-vation as the innovation processes are inﬂu-enced by many institutions and economic agents.In addition,they are often path-dependent as small events are reinforced and become crucially important through processes of positive feedback,in line with evolutionary processes as discussed in Section3.1.Evolutionary MetaphorFinally,most NIS approaches do not explicitly use the biological metaphor.Nevertheless, many of the approaches are based on innova-tion theories in which they do use an explicit evolutionary metaphor(e.g.,the work of Nelson).To summarize,the current(policy)NIS approaches have already implicitly incorpo-rated some evolutionary notions such as non-optimality,novelty and gradualism.However, what is missing is a more explicit analysis of the different institutional levels of the economic system and innovation subsystems (their inertia and evolution)and how they change over time in interaction with the various learning activities of economic agents. These economic agents reside at established ﬁrms,start-upﬁrms,universities,govern-ments,undertaking learning and innovation activities or strategic actions.The explicit use of the biological metaphor and an explicit use of the methodological interactionst approach may increase our understanding of the evolu-tion of innovation systems.Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing4.Towards a Dynamic View of Universities4.1The Logic of an Endogenous‘Learning’UniversityIf we translate the methodological interaction-ist approach to the changing role of universities in an evolutionary innovation system,it follows that universities not only respond to changes of the institutional environment(government policies,business demands or changes in scientiﬁc paradigms)but universities also inﬂuence the institutions of the selection envi-ronment by their strategic,scientiﬁc and entre-preneurial actions.Moreover,these actions inﬂuence–and are inﬂuenced by–the actions of other economic agents as well.So,instead of a one-way rational response by universities to changes(as in reductionist approach),they are intertwined in those processes of change.So, universities actually function as an endogenous source of change in the evolution of the inno-vation system.This is(on an ontological level) a fundamental different view on the role of universities in innovation systems from the existing policy NIS frameworks.In earlier empirical research,we observed that universities already effectively function endogenously in evolutionary innovation system frameworks;universities as actors (already)develop new knowledge,innovate and have their own internal capacity to change,adapt and inﬂuence the institutional development of the economic system(e.g., V an der Steen et al.,2009).Moreover,univer-sities consist of a network of various actors, i.e.,the scientists,administrators at technology transfer ofﬁces(TTO)as well as the university boards,interacting in various ways with indus-try and governments and embedded in various ways in the regional,national or inter-national environment.So,universities behave in an at least partly endogenous manner because they depend in complex and often unpredictable ways on the decision making of a substantial number of non-collusive agents.Agents at universities react in continuous interaction with the learn-ing activities ofﬁrms and governments and other universities.Furthermore,the endogenous processes of technical and institutional learning of univer-sities are entangled in the co-evolution of institutional and technical change of the evo-lutionary innovation system at large.We propose to treat the learning of universities as an inseparable endogenous variable in the inno-vation processes of the economic system.In order to structure the endogenization in the system of innovation analysis,the concept of the Learning University is introduced.In thenext subsection we discuss the main character-istics of the Learning University and Section5discusses the learning university in a dynamic,evolutionary innovation system.An evolution-ary metaphor may be helpful to make theuniversity factor more transparent in theco-evolution of technical and institutionalchange,as we try to understand how variouseconomic agents interact in learning processes.4.2Characteristics of the LearningUniversityThe evolution of the involvement of universi-ties in innovation processes is a learningprocess,because(we assume that)universitypublic agents have their‘own agenda’.V ariousincentives in the environment of universitiessuch as government regulations and technol-ogy transfer policies as well as the innovativebehaviour of economic agents,compel policymakers at universities to constantly respondby adapting and improving their strategiesand policies,whereas the university scientistsare partly steered by these strategies and partlyinﬂuenced by their own scientiﬁc peers andpartly by their historically grown interactionswith industry.During this process,universityboards try to be forward-looking and tobehave strategically in the knowledge thattheir actions‘inﬂuence the world’(alsoreferred to earlier as‘intentional variety’;see,for instance,Dosi et al.,1988).‘Intentional variety’presupposes that tech-nical and institutional development of univer-sities is a learning process.University agentsundertake purposeful action for change,theylearn from experience and anticipate futurestates of the selective environment.Further-more,university agents take initiatives to im-prove and develop learning paths.An exampleof these learning agents is provided in Box1.We consider technological and institutionaldevelopment of universities as a process thatinvolves many knowledge-seeking activitieswhere public and private agents’perceptionsand actions are translated into practice.3Theinstitutional changes are the result of inter-actions among economic agents deﬁned byLundvall(1992)as interactive learning.Theseinteractions result in an evolutionary pattern3Using a theory developed in one scientiﬁc disci-pline as a metaphor in a different discipline mayresult,in a worst-case scenario,in misleading analo-gies.In the best case,however,it can be a source ofcreativity.As Hodgson(2000)pointed out,the evo-lutionary metaphor is useful for understandingprocesses of technical and institutional change,thatcan help to identify new events,characteristics andphenomena.Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing。

以学生为本构建和谐发展的学生工作体系_田波

价值工程以学生为本构建和谐发展的学生工作体系To Establish Students-oriented Work System of Harmonious Development田波Tian Bo（哈尔滨工程大学材料科学与化学工程学院，哈尔滨150001）（Harbin Engineering University,Institute of Material Science and Chemical Engineering,Harbin150001,China）摘要：在新形势、新环境的影响下，社会人才需求发生了重大的变化，给高等教育创新带来了更高的要求。

新时期高校的学生工作，必须认清当前所面临的形势和任务，解放思想，实事求是，牢固树立“以学生为本”的科学发展观。

以学生为本是以人为本的科学发展观在高校中的体现，如何把学生培养综合素质高，能适应社会建设需要的毕业生，对高校辅导员提出了新要求。

把科学发展观落实到工作中，树立以学生为本的理念，是构建和谐发展的学生工作体系的根本，是高校为社会培养可靠顶用之才的关键。

Abstract:Social talents have undergone major changes in the new situation and under the influence of the new environment,which has brought ahigher demand on higher education innovation.For students work in College in the new age,we must understand the current situation and tasks,emancipating the mind,seeking truth from facts,and firmly establishing the"student-centered"concept of scientific development.Student-oriented is thescientific concept of people-centered embodied in the college,how to cultivate students into graduate with comprehensive high quality and could adapt tocommunity-building needs propose new requirements on college counselors.Implementing the scientific concept of development work and fosteringstudent-centered philosophy is the fundamental system of harmonious development of student work,is the key for colleges to train application-orientedtalents for society.关键词：科学发展观；以学生为本；高校；辅导员；工作体系Key words:scientific concept of development;student-centered;colleges and universities;counselor;work system中图分类号：G45文献标识码：A文章编号：1006-4311（2011）03-0222-02对策二：充分考虑职校学生实际，任务设计循序渐进。

大学的变化英语作文

In recent years,universities have undergone significant transformations,reflecting the evolving needs of society and advancements in technology.Here are some of the key changes that have shaped the modern university landscape:1.Technological Integration:The use of technology in classrooms has become ubiquitous. Lectures are often supplemented with online resources,and students can access course materials through digital platforms.Interactive tools and software have become integral to the learning process.2.Diversification of Learning Methods:Traditional lectures are no longer the only method of instruction.Universities now offer a variety of learning experiences,including seminars,workshops,and online courses.This diversity caters to different learning styles and preferences.3.Globalization:Universities have become more international,with an increase in the number of international students and faculty.This has led to a more diverse campus environment and a greater emphasis on global perspectives in teaching and research.4.Research Focus:There has been a shift towards researchintensive education. Universities are now not just places of learning but also hubs for innovation and research, often collaborating with industries and governments to address global challenges.5.Flexibility in Study:With the rise of online and distance learning,students can now pursue higher education at their own pace and from anywhere in the world.This flexibility has made education more accessible to a wider range of people.6.Emphasis on Employability:Universities are increasingly focusing on preparing students for the job market.This includes offering internships,career guidance,and courses that are aligned with industry needs.7.Sustainability:There is a growing awareness of the need for sustainable practices. Many universities are adopting green initiatives,such as reducing waste,conserving energy,and integrating environmental studies into their curriculum.8.Student Services:The range of services offered to students has expanded to include mental health support,career counseling,and extracurricular activities that promote personal development and wellbeing.9.Financial Models:The cost of higher education has risen,leading to debates about affordability and the value of a university degree.Some institutions are exploringalternative financial models,such as income share agreements.10.Campus Life:The social aspect of university life has also evolved,with a greater focus on community engagement,studentled initiatives,and the development of campus facilities that promote interaction and collaboration.These changes reflect the dynamic nature of higher education and its ability to adapt to the changing world.Universities are not just adapting to these changes but are also driving them,shaping the future of education and research.。

211188574_中国膳食模式的特征、分布及其与健康相关性研究进展

马志敏，郝晓燕，王东阳，等. 中国膳食模式的特征、分布及其与健康相关性研究进展[J]. 食品工业科技，2023，44（10）：396−405.doi: 10.13386/j.issn1002-0306.2022060202MA Zhimin, HAO Xiaoyan, WANG Dongyang, et al. Evolution and Distribution of Dietary Patterns in China and the Research Progress of Its Correlation with Health[J]. Science and Technology of Food Industry, 2023, 44(10): 396−405. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2022060202· 专题综述 ·中国膳食模式的特征、分布及其与健康相关性研究进展马志敏1，郝晓燕2，王东阳3，王秀丽3，孙奕良3，李海燕3,*（1.中国农业大学，北京 100083；2.中国粮食研究培训中心，北京 100834；3.农业农村部食物与营养发展研究所，北京 100081）摘　要：随着社会进步和生活水平的提高，中国不同地区的居民膳食模式呈现差异，仍存在营养不均衡、慢性病患者基数增加的现象。

本文通过调研文献汇总中国不同地区居民的膳食模式特征、分布及与常见慢性病的相关研究进展，针对膳食结构存在不合理的人群提出营养指导。

对于不同地区的人群而言，更推荐烹饪少盐清淡，并以豆制品、水产品、奶类、果蔬为主的东方健康膳食模式。

对不同年龄人群而言，婴幼儿需要补充果蔬、豆类保证膳食健康，更推荐能量为主的营养均衡膳食模式和以优质蛋白为主的西方膳食模式；青少年要减少油炸食品摄入，更推荐富含奶类、果蔬的多样性膳食模式；中老年要注重增加蛋白质、膳食纤维、脂肪和肉禽类的摄入，更推荐以果蔬和全谷物为主的膳食模式。

组织结构变革中的路径依赖与路径创造机制研究——以联想集团为例

组织结构变革中的路径依赖与路径创造机制研究——以联想集团为例李海东;林志扬【摘要】Due to the strong tendency of historical determinism, the classical path dependence theory can not explain the significant technical and institutional change and the generation of a new path. These issues promote the researchers to switch the research perspective and pay more attention to the path creation and path breaking. Strategic action has a attribute of path dependence, and according to the contention that strategy determines structure and structure follows strategy, the article illustrates that path dependence is embedded in organizational structure system. From the perspective of the organizational structure model evolution! the mechanism of path dependence formation and path creation is discussed. At the same time, the dual impact of path dependence and path creation in organizational structure change on organizational operation is also discussed. Finally, the article takes Lenovo Group of China modern IT industry as an example to illustrates path dependence and path creation in the evolution process of Lenovo Group's organizational structure model.%经典的路径依赖理论因具有较强的历史决定论倾向,因而无法解释重大的技术和制度变革以及新路径的产生,这些问题推动着研究者将研究视角转向了路径创造和路径突破.战略行为具有路径依赖的特征,根据“战略决定结构、结构跟随战略”的思想,组织结构系统内生地蕴含着路径依赖特性.从组织结构模式演进的角度对组织中的路径依赖形成机制和路径创造机制进行研究,并讨论了组织结构变革中的路径依赖和路径创造对组织运行的双重影响.以联想集团为例,探讨了联想集团组织结构模式选择演化历程中的路径依赖和路径创造.【期刊名称】《管理学报》【年(卷),期】2012(009)008【总页数】12页(P1135-1146)【关键词】路径依赖;组织结构变革;路径创造;自我增强机制;联想集团【作者】李海东;林志扬【作者单位】景德镇陶瓷学院工商管理学院;厦门大学管理学院【正文语种】中文【中图分类】C93组织作为一个开放性系统，不断地与外部环境进行物质、能量和信息的交换。

发展休闲农业_促进城乡协调发展_白硕

Develop Recreational Agriculture and Promote the Coordinated Development of Urban and Rural AreasShuo BAI, Le WANG, Xiaoxia MA1. Research Centre of Rural Economics and Management, Southwest University, Chongqing, 400715, China2. Research Centre of Rural Economics and Management, Southwest University, Chongqing, 400715, China3. Research Centre of Rural Economics and Management, Southwest University, Chongqing, 400715, China1. baishuo@,2. wangleler@,3. maxia316@Abstract: This study discusses the necessity of developing recreational agriculture in central and western regions, then analyzes the problems of it and finally puts forward the corresponding strategies to address these issues systematically. Based on the theoretical and empirical analyses above, this study provides a systematical thinking paradigm for the development of recreational agriculture and the promotion of the coordinated development of urban and rural areas in Midwest part of China. Keywords: Recreational Agriculture; Central and Western Regions; Strategies发展休闲农业，促进城乡协调发展白硕，王乐，麻晓霞1. 西南大学农村经济与管理研究中心,重庆,中国,40071512. 西南大学农村经济与管理研究中心,重庆,中国,4007153. 西南大学农村经济与管理研究中心,重庆,中国,400715 1. baishuo@, 2. wangleler@, 3. maxia316@【摘要】本文首先探讨了中西部地区发展休闲农业的必要性，随后分析了中西部地区休闲农业发展所存在的问题，最后系统地提出了解决这些问题的相应策略。

Innovation systems analytical and methodological issues

Research Policy 31(2002)233–245Innovation systems:analytical and methodological issues ଝBo Carlsson a ,∗,Staffan Jacobsson b ,Magnus Holmén b ,Annika Rickne baDepartment of Economics,Weatherhead School of Management,Case Western Reserve University,Cleveland,OH 44106-7206,USAb Department of Industrial Dynamics,Chalmers University of Technology,Cleveland,S-41296Gothenburg,SwedenAbstractInnovation systems can be deﬁned in a variety of ways:they can be national,regional,sectoral,or technological.They all involve the creation,diffusion,and use of knowledge.Systems consist of components,relationships among these,and their characteristics or attributes.The focus of this paper is on the analytical and methodological issues arising from various system concepts.There are three issues that stand out as problematic.First,what is the appropriate level of analysis for the purpose at hand?It matters,for example,whether we are interested in a certain technology,product,set of related products,a competence bloc,a particular cluster of activities or ﬁrms,or the science and technology base generally—and for what geographic area,as well as for what time period.The choice of components and system boundaries depends on this,as does the type of interaction among components to be analyzed.The attributes or features of the system components that come into focus also depend on the choice of level of analysis.The second and closely related issue is how to determine the population,i.e.delineate the system and identify the actors and/or components.What are the key relationships that need to be captured so that the important interaction takes place within the system rather than outside?The third issue is how to measure the performance of the system.What is to be measured,and how can performance be measured at the system level rather than at component level?©2002Elsevier Science B.V .All rights reserved.Keywords:Innovation systems;Geographical dimension;Components;Delineation;Unit of analysis1.IntroductionThe systems approach to the analysis of economic and technological change is not new.Several systems approaches have been suggested in the literature.A system may be deﬁned as “a set or arrangement of things so related or connected as to form a unity or organic whole”(Webster’s Collegiate Dictionary).Given that different systems serve different purposes,ଝPaper presented at the DRUID conference,Rebild Bakker,Denmark,June 1999.∗Corresponding author.Tel.:+1-216-368-4112;fax:+1-216-368-5039.E-mail address:bxc4@ (B.Carlsson).it is not surprising that there exists a variety of systems concepts.The object of this paper is to review some of the most important analytical and methodological issues which arise in applying a systems approach to the analysis of technological innovation.Systems of innovation can be viewed in several dimensions.One important dimension is the physical or geographical dimension.Sometimes the focus is on a particular country or region which then deter-mines the geographic boundaries of the system.In other cases the main dimension of interest is a sector or technology.In such cases,the determination of the relevant geographic boundaries is itself a theoretical or at least methodological issue.Due to the vast im-provements in communication technology in recent0048-7333/02/$–see front matter ©2002Elsevier Science B.V .All rights reserved.PII:S 0048-7333(01)00138-X234 B.Carlsson et al./Research Policy31(2002)233–245decades,there is an international dimension to almost any economic activity.How to delineate a system is therefore an important issue.Another dimension is that of time.In a system with built-in feedback mechanisms,the conﬁguration of components,attributes,and relationships is constantly changing.Thus,a snapshot of the system at a particu-lar point in time may differ substantially from another snapshot of the same system at a different time.In the literature on systems of innovation there has not been much explicit discussion of the function or purpose of each system,nor of what constitutes inputs and outputs of the system.As a result,there is not much discussion of system performance either. Certainly,it is of great interest to measure or at least assess performance when similar systems are com-pared.Thus,the measurement of system performance raises another set of issues.The paper is organized as follows.In the next section,we deﬁne what we mean by a‘system’.We then review a variety of concepts of innovation sys-tems which have appeared in the literature.This is followed by a discussion of common methodological issues arising in the empirical application of the sys-tem of innovation concept.We conclude with a brief summary of the mainﬁndings.2.What is a system?Systems engineers deﬁne a system as a set of interrelated components working toward a common objective.Systems are made up of components,rela-tionships,and attributes.Components are the operating parts of a system. They can be of a variety of types:actors or orga-nizations such as individuals,businessﬁrms,banks, universities,research institutes,and public policy agencies(or parts or groups of each).They can be physical or technological artifacts such as turbogener-ators,transformers,and transmission lines in electri-cal power systems and biomedical devices,diagnostic techniques,and drugs in biomedical/biotechnological systems.They can also be institutions in the form of legislative artifacts such as regulatory laws,traditions, and social norms.Relationships are the links between the components. The properties and behavior of each component of the set inﬂuence the properties and behavior of the set as a whole.At the same time,each component depends upon the properties and behavior of at least one other component in the set.Because of this interdependence, the components cannot be divided into independent subsets;the system is more than the sum of its parts (Blanchard and Fabrycky,1990,p.2).Also,if a com-ponent is removed from a system or if its characteris-tics change,the other artifacts in the system will alter characteristics accordingly(Hughes,1987,p.51), and the relationships among them may also change—provided that the system is robust.A non-robust sys-tem would simply collapse if an essential component were removed.Thus,a function(say venture capitalﬁ-nance)which is carried out by a particular set of actors in speciﬁc forms may be carried out by another set of actors and under different arrangements in a similar system at a different time or in a different place. Relationships involve market as well as non-market links.Feedback(interaction)is what makes systems dynamic;without such feedback,the system is static. Put differently,the greater the interaction among the components of a system,the more dynamic it is.But even a highly dynamic system may not be able to survive,unless it evolves in the right direction.One of the most important types of relationships in innovation systems involves technology transfer or acquisition,some of which takes place via markets, some via non-market interaction.Indeed,one could argue that technology transfer is the core activity in an innovation system.Some technology may be transferred unintentionally or accidentally;in such cases the term“technological spillovers”may be appropriate.In other cases the technology transfer is clearly intentional for both supplier and receiver. But in neither case can it happen without consider-able investment in time and effort by the recipient to attain the required receiver competence.Technology acquisition usually involves a collaborative process of some duration,not a once-for-all transaction. One result of interaction(feedback)among actors is that capabilities shift and grow over time,and therefore,the system conﬁguration also changes. Attributes are the properties of the components and the relationships between them;they characterize the system.“Because the components of a technologi-cal system interact,their characteristics derive from the system”(Hughes,1987,p.52).In other words,B.Carlsson et al./Research Policy31(2002)233–245235the features which are crucial for understanding the system are related to the function or purpose served by the system,as well as the dimensions in which it is analyzed.The function of an innovation system is to generate,diffuse,and utilize technology.Thus, the main features of the system are the capabilities (together representing economic competence)of the actors to generate,diffuse,and utilize technologies (physical artifacts as well as technical know-how) that have economic value.Economic(or techno-economic)competence is deﬁned as the ability to identify and exploit business opportunities(Carlsson and Eliasson,1994).This in-volves four types of capability.Theﬁrst is selective (or strategic)capability:the ability to make innova-tive choices of markets,products,technologies,and organizational structure;to engage in entrepreneurial activity;and to select key personnel and acquire key resources,including new competence.The key ques-tion here is one of effectiveness:are we doing the right thing?Too often this capability is simply as-sumed in the economic literature to exist and to be evenly distributed acrossﬁrms—clearly contrary to what we observe.An important part of this capability is the notion of receiver competence or absorptive capacity:the ability to scan and monitor relevant technological and economic information,to identify technical and market opportunities,and to acquire knowledge,information,and skills needed to develop technologies.The second element of economic or techno-econo-mic competence is organizational(integrative or coordinating)ability.This is the main function of middle management in an organization:to organize and coordinate the resources and economic activities within the organization so that the overall objectives are met.This includes the ability to generate and improve technologies through new combinations of existing knowledge and skills.The third element is technical or functional ability. It involves the efﬁcient execution of various functions within the system to implement technologies and uti-lize them effectively in the market.The key question here is that of efﬁciency:are we doing things right? The fourth element is the learning(or adaptive) ability,the ability to learn from success as well as fail-ure,to identify and correct mistakes,to read and inter-pret market signals and take appropriate actions,and to diffuse technology throughout the system.1This ability is essential for long-term survival.Aﬁrm which is both effective and efﬁcient at a point in time eventu-ally becomes neither,unless it can adapt to changing circumstances(especially changing technology). The dynamic properties of the system—robustness,ﬂexibility,ability to generate change and respond to changes in the environment—are among its most important attributes.Change can be generated endo-genously:new components(actors,technological artifacts)are introduced while others exit;the relation-ships among the components change;and the attributes (capabilities of actors,nature and intensity of links among actors)change.Similar changes can be induced or necessitated by changes in the environment,e.g. the changes in the nature and frequency of interaction among entities made possible through the Internet.3.Various systems approachesOne of the earliest system concepts used in the liter-ature is that of input/output analysis(Leontief,1941), focusing on theﬂows of goods and services among sectors in the economy at a particular point in time. Here,it is clear what the inputs and outputs are and how the system is conﬁgured.The components and relationships in the system are viewed at the meso (industry)level.The links among the components of the system are basically one-way,i.e.the system is static.Another early approach is that represented by ‘development blocs’as deﬁned by Dahmén(1950): sequences of complementarities which by way of a series of structural tensions,i.e.disequilibria,may result in a balanced situation(Dahmén,1989,p. 111).The basic idea is that as an innovation creates new opportunities,these opportunities may not be realized(converted into economic activity)until the pre-requisite inputs(resources and skills)and product markets are in place.Each innovation,therefore,gives rise to a‘structural tension’which,when resolved, makes progress possible and may create new tensions and which,if unresolved,may bring the process to 1Chang(1999)uses a similar terminology at the system level, distinguishing between absorptive,combinative,implementation, and endogenizing capabilities.236 B.Carlsson et al./Research Policy31(2002)233–245a halt.Thus,while the input/output analysis is static, Dahmén’s concept already is dynamic,representing one of theﬁrst attempts to apply Schumpeterian anal-ysis.It incorporates the notion of disequilibrium and focuses on the role of the entrepreneur.The output of the system not only grows over time but also changes in character and content.Dahmén’s analysis focuses on the inter-war period(1919–1939),not just a single year,and it is highly disaggregated,covering the struc-tural development of24different industries in Sweden.A third but much later approach is widely known as national innovation systems(Freeman,1988; Lundvall,1988,1992;Nelson,1988,1993;and subse-quently many others).Here,the framework is broad-ened beyond the input/output system to include not only industries andﬁrms,but also other actors and organizations,primarily in science and technology,as well as the role of technology policy.The analysis is carried out at the national level:R&D activities and the role played by universities,research institutes,govern-ment agencies,and government policies are viewed as components of a single national system,and the link-ages among these are viewed at the aggregate level. Because of the size and complexity of the system(and therefore,the large number of linkages among com-ponents at lower levels of aggregation),the empirical emphasis in the studies carried out thus far is mainly on statics or comparative statics.But there is nothing in principle preventing a more dynamic analysis. Another approach is represented by Michael Porter’s‘diamond’,described in his1990book(The Competitive Advantage of Nations).The four sides of the diamond are made up of factor conditions(skills, technologies,capital,etc.),demand conditions(es-pecially“competent demand”as represented,e.g.by technically sophisticated customers),links to related and supporting industries,andﬁrm strategies,struc-ture,and rivalry.Each economic activity is viewed primarily as an industry,but is also as part of a cluster of activities and agents rather than as taking place in isolation.Because of the industry focus, Porter strongly emphasizes the role of competition among actors within industries(i.e.market competi-tion)while suppressing non-market interaction with entities outside the industry.In this sense,the system deﬁnition is narrower than in the national innovation system approach.Again,the main focus is on a static or comparative static analysis.A similar approach is represented by‘sectoral innovation systems’(Breschi and Malerba,1997;see also Malerba and Orsenigo,1990,1993,1995).As in Porter’s analysis,the system deﬁnition here is based on‘industry’or‘sector’.But rather than focusing on interdependence within clusters of industries,sectoral innovation systems are based on the idea that different sectors or industries operate under different techno-logical regimes which are characterized by particular combinations of opportunity and appropriability con-ditions,degrees of cumulativeness of technological knowledge,and characteristics of the relevant knowl-edge base.These regimes may change over time, making the analysis inherently dynamic,focusing on the competitive relationships amongﬁrms by explic-itly considering the role of the selection environment. Another system deﬁnition is built around the concept of local industrial systems as represented in AnnaLee Saxenian’s(1994)study of the electronics industry in Silicon Valley in California and along Route128in Massachusetts.Here,the system def-inition is primarily geographical.The focus is on differences in culture and competition which have led to differences among the two regions in the degree of hierarchy and concentration,experimentation,collab-oration,and collective learning which,in turn,have entailed differences in the capacity to adjust to chang-ing circumstances in technology and markets.Thus, the analysis is inherently dynamic,but not in a formal sense.Finally,the approach on which we will focus in more detail here is that based on the notion of techno-logical systems(Carlsson,1995,1997).This concept is similar to Erik Dahmén’s‘development blocs’(Dahmén,1950,1989)in that it is both disaggregated and dynamic:there are many(or at least several)tech-nological systems in each country(thus,differing from national innovation systems2);and they evolve over time,i.e.the number and types of actors,institutions, relationships among them,etc.vary over time(thus, differing from all the other system deﬁnitions except Dahmén’s).Also,national borders do not necessar-ily form the boundaries of the systems.In addition, the system deﬁnition focuses on generic technologies 2It is,however,possible,at least in principle,to view a national system of innovation as the aggregate of a set of technological, sectoral,or regional systems.B.Carlsson et al./Research Policy31(2002)233–245237with general applications over many industries,thus, distinguishing it from all the other approaches. Technological systems involve market and non-market interaction in three types of network:buyer–supplier(input/output)relationships,problem-solving networks,and informal networks.While there may be considerable overlap between these networks,it is the problem-solving network which really deﬁnes both the nature and the boundaries of the system: where do various actors in the system turn for help in solving technical problems?Buyer–supplier linkages are important,the more so the more technical infor-mation is transmitted along with the transactions and less so,the more commodity-like the transactions are. Sometimes the most important technical information comes from sources(e.g.universities and research in-stitutes)separate from buyers and sellers.Sometimes the informal,mostly personal,networks established through professional conferences,meetings,publi-cations,etc.are important channels of information gathering and sharing.A closely related concept is that of competence petence blocs are deﬁned primarily from the demand(product or market)side as the total infra-structure needed to create,select,recognize,diffuse, and exploit new ideas in clusters ofﬁrms(Eliasson and Eliasson,1997,p.14).An example is the com-petence bloc for health care in Sweden(see Eliasson, 1997).This bloc can be viewed as consisting of parts of several technological systems supplying techno-logical artifacts applicable in the health care sector. In applying the technological systems approach we start with the following four basic assumptions:1.The system as a whole(rather than its individualcomponents)is the primary unit of analysis;this is similar to several of the other systems approaches.2.The system is dynamic,i.e.we need to take feed-back explicitly into account.This becomes es-pecially important in our approach,since we are interested in the evolution of the system over time.3.Global technological opportunities are practicallyunlimited,i.e.the contribution of the system to global knowledge is quite modest;the main focus is on how well the system can identify,absorb, and exploit global technological opportunities.This means,e.g.that it may be more important to raise absorptive capacity than to create new technology.4.Each actor(component)in the system operates withbounded rationality:actors are rational,but act un-der constraints of limited capabilities,information, etc.This is especially important in view of the vast global opportunity set.4.Methodological issuesThere are a number of methodological issues which arise in the application of the analytical framework of technological systems.The framework is still fairly loosely deﬁned and several methodological alterna-tives are available.In this section,we will focus primarily on technological systems,but the issues are similar in other systems approaches as well.Our aim is to contribute to the discussion of methodology with respect to the analysis of innovation systems.In our studies of technological systems over the last decade,there are three methodological issues which stand out as the most problematic.Theﬁrst is the level of analysis to which a system approach is applied.A second is how we deﬁne the system boundaries,i.e. how to delineate the system and identify the actors.A third is how we can measure the performance of the system.4.1.The level of analysisThe technological system framework was deﬁned originally as a network of agents interacting in a spe-ciﬁc technology under a particular institutional infras-tructure and involved in the generation,diffusion and utilization of technology(Carlsson and Stankiewicz, 1995,p.49).This deﬁnition opens up for a number of different ways to delineate the system,each involving a different level of analysis.We have found that the system approach may fruitfully be applied to at least three levels of analysis:to a technology in the sense of a knowledgeﬁeld,to a product or an artifact,or ﬁnally to a set of related products and artifacts aimed at satisfying a particular function,such as health care or transport(this third level of analysis is henceforth labeled a competence bloc,see Eliasson,1997).33A fourth unit of analysis is a set of relatedﬁrms(vertically or horizontally linked)operating on different markets and serving different functions.This is the unit of analysis employed by Porter (1998)in cluster analysis.238 B.Carlsson et al./Research Policy31(2002)233–245Fig.1.Illustration of the three levels of analyses.The differences between these three approaches is schematically shown in Fig.1where the‘columns’indicate products(P1,P2,etc.),the‘boxes’technolo-gies(T1,T2,etc.)which are used within these appli-cations,and the circles the customers(C1,C2,etc.) which the products aim to address.To delineate the system we may take as a starting point a speciﬁc tech-nology(or set of closely related technologies),in the sense of a speciﬁc knowledgeﬁeld,and analyze it in a certain application or in all its uses(e.g.technology T1is used in product P1as well as in P2and P3).An example of a knowledgeﬁeld may be digital signal processing,which may be used in a number of dif-ferent products(e.g.mobile phones,control systems, etc.).Even though the process in which the technology is diffused into these products is analyzed,the prod-ucts are not the main focus when this level of analysis is applied.Instead,we may penetrate the relation between technologies and the diffusion of technolo-gies into different applications.For instance,Holmén (1998)studied microwave antenna technology which is incorporated into many and highly diverse products including mobile phones,microwave ovens,military radar and automatic doors.The customers included will be all those for which this technology is impor-tant.For example,if technology T1is object of the study,parts of customer groups C1–C7may have to be considered.The second level of analysis is when we take a prod-uct as the initial seed from which the system is deﬁned.For example,an industrial robot(say P1in theﬁgure) consists of a number of technologies,e.g.drive,sensor and control technologies(T1,T3and T4in theﬁgure), but the technologies are not the main focus when this level of analysis is applied.Instead,it is the artifact which is studied,and we want to study the links to its customers(in this case customer groups C1and C2). If we instead are interested in a speciﬁc market and the system of actors and institutions supplying products to this market,we need to use the third level of analysis. This is the multi-product case where the focus is on a set of products(complementary or substitute)which are related by having a common market,say the health care market,operate under the same institutional ar-rangement,and therefore,share a common selection environment.In Fig.1,products1–4could all be in the health care market,and we would include all products as well as all technologies in the analysis.(This level of analysis has been termed a competence bloc.)In such a study,we may analyze relations between products, between all customers groups,and also between the customers.However,the competence bloc will include a vast range of technologies and it will not be feasi-ble to have a detailed analysis on the technological level.Ourﬁrst empirical work focused on factory auto-mation equipment such as numerically controlled machine tools and industrial robots(Carlsson,1995). The technologies involved were,for instance,control engineering,software engineering,and machine de-sign.Hence,we ended up using a product as the level of analysis.As a part of our research after this initial study, some members of our team took a direction where the level of analysis was constituted by a technology, in the sense of a knowledgeﬁeld.This approach was pursued in Granberg(1997),Holmén and Jacobsson (1998),Holmén(1998)as well as by Rickne(2001), Laestadius(2001)and Fridh(2001).Another direction of research was towards the multi-product case,the competence bloc(Eliasson,1997).Thus,the system boundaries,the actors involved, the networks and institutions may vary depending on how we choose the level of analysis.With technology as the level of analysis,on the other hand,we will include all those entities having competence within a certain technologicalﬁeld(e.g.control engineer-ing),regardless of in which application(products,B.Carlsson et al./Research Policy31(2002)233–245239such as robots andﬁghter planes)they have used this competence.Taking a product focus,the actors are all within a given industry(e.g.the machine tool industry).Finally,a competence bloc as the level of analysis would include actors from several industries.By focusing on technology in the sense of a speciﬁc knowledgeﬁeld,we clearly cut a different‘slice of the cake’than had the(multi-technological)product been chosen as the level of analysis.This is illustrated in Holmén and Jacobsson(1998).This study attempted to identify the actors in a technological system where the level of analysis was microwave antenna technol-ogy.Altogether,over the20years covered,a total of 35ﬁrms and other actors were identiﬁed at one time or another as belonging to the system in the sense that they were judged to have the capability to develop mi-crowave antenna technology.The27ﬁrms(eight ac-tors were individuals or institutions)were classiﬁed in as many as11three-digit industries(SNI92).4Only four of the actors were classiﬁed in industry classes 322(manufacturing of radio and TV transmitters,and apparatus for wired telephony and telegraphy)or323 (manufacturing of radio and TV receivers,and ap-paratus for reproduction of audio and video signals) which pertain to telecommunication and which would be theﬁrst industries to look forﬁrms mastering this technology.The others were in a wide range of other industries.Similar observations can be made for other technology-based clusters such as those based on polymers and biomedicine which also cut across industries.The choice of unit of analysis partly reﬂects the nature of the questions raised.Referring back to the deﬁnition of technological systems as“...generating, diffusing and using technology...”,a knowledge-based approach(theﬁrst level of analysis)would tend to be more concerned with technological problem-solving activities and the generation of new competence and knowledge(see for instance Granberg,1995)whereas a product focus would tend to be chosen when the primary interest is in diffusion and use of new technology,see for instance Carlsson and Jacobsson(1993).4An‘industry’is broadly equivalent to the four digit level in ISIC,e.g.35.1‘building and repair of ships and boats’.4.2.System boundaries:the delineation of a technological system and identiﬁcation of actors The second issue which we have encountered in our work is how to delineate the systems,i.e.setting the boundaries of the system.When the focus is on a prod-uct group or set of vertically related product groups,as is often the case within the context of National Inno-vation Systems or Porter’s‘diamond’,the delineation seems not to be a large issue as standard industrial and trade classiﬁcations can be used(Porter,1990).A case in point would be the forest cluster in Finland which consists of key products such as paper and pulp and upstream and downstream industries such as paper machines and printing plants(Ylä-Anttila, 1994).Another example is our earlier work on factory automation(Carlsson,1995).Still,if the ambition is to include a whole cluster of relatedﬁrms,there is always the issue of where the boundary of the system lies.There is no reason to hide that the delineation may often be somewhat arbitrary and partly based on informed guesses by the researcher(Porter,1998).5 Delineating the technological system is more dif-ﬁcult when the level of analysis is a speciﬁc techno-logy.Three issues are at hand,each of which will be discussed below.4.2.1.What is a technology,i.e.what falls withina particular knowledgeﬁeld?To be able to delineate the system,we need to understand what are the boundaries of the knowledge ﬁeld studied,i.e.what falls inside and outside a par-ticular knowledgeﬁing technology as the level of analysis necessarily involves making such judg-ments in the process of delineating the technological system.This can,of course,not be done unless the researcher is familiar with the technologicalﬁeld and interacts a great deal with technological specialists. One way of doing this would be to assess the distance in terms of knowledge between various technologies and set a‘break’point in this contin-5As Porter(1998,p.202)puts it:“drawing cluster boundaries is often a matter of degree,and involves a creative process informed by understanding the most important linkages and complemen-tarities across industries and institutions to competition.The strength of these‘spillovers’and their importance to productivity and innovation determine the ultimate boundaries”.。

新时代应用型高校高素质教师队伍建设探究

第37卷第2期2〇19年3月西'航空学院学报Journal of Xi an Aeronautical UniversityVol. 37No. 2Mar. 2019新时代应用型高校高素质教师队伍建设探究雷文静，周兴志，李中(西安航空学院人事处，西安H0077)摘要：：随着中国特色社会主义进入新时代，我国高等教育规模不断扩大并且进入多类型快速发展时期。

新时代下，应用型高校要顺应时代的发展，培养符合社会经济发展需求的各类高技能人才，必须建立高素质的教师队伍，这是应用型高校实现创新发展的基础和关键因素。

通过剖析高素质教师队伍的内涵和应用型高校对高素质教师队伍的要求，探索研究了应用型高校建设高素质教师队伍的方法和有效路径。

关键词：新时代;应用型高校；高素质教师队伍；经济发展需求中图分类号：G648. 4 文献标识码:A文章编号=1008-9233(2019)02-0088-05Study on the Construction of High-Quality Teaching Staff inApplication-oriented Colleges and Universities in the New EraL E I W en-jing , ZH O U Xing-zhi , L I Zhong(Department of Personnel,Xi’anAeronauticalUniversity，Xi’an710077，China)Abstract：Since socialism with Chinese characteristics has entered the new era，the scale of China^ s higher education has continuously expanded and entered a period of rapid development in multiple types.In the new era，application-oriented colleges and universities should conform to opment of the times and cultivate high-skilled talents that meet the demand of social and economic development.Therefore,tt is necessary to establish a high-quality teaching st and key factor for the development of application-oriented colleges and universities.Through ana-yzing the connotation and requirements of high-quality teaching staff，this paper explores themethods and effective paths of building a high-quality teaching staff in application-oriented colleges and universities.Key words：new era；application-oriented col^ges and universities；high-quality teaching staff；demand of economic development现阶段，我国社会经济发展正处于重要的转型变革时期，需要大量的应用创新型人才为经济结构调整和产业升级服务。

从产业园区向产业社区转型的创新战略路径研究——以紫竹国家高新技术产业开发区研发基地二期为例

140 | 产业园区Research on the Strategic Path of Innovation Transformation from Industrial Park to Industrial Community: A Case Study of the R&D Base (II Phase) of ZizhuNational High-tech Industrial Development Park从产业园区向产业社区转型的创新战略路径研究——以紫竹国家高新技术产业开发区研发基地二期为例袁芯 YUAN Xin围绕上海科创中心建设，探索城市规划如何服务和引领传统产业园区向综合产业社区的全面升级发展。

秉承“以小更新谋大效益”的理念，创新性地提出在开展产业园区的局部更新之前，制定一个园区及周边区域整体层面、涵盖战略到行动的技术框架，突出战略性、传导性与实施性。

通过制定“目标—策略—方案—行动”的技术框架，加强核心功能引领与多维空间适配，建立转型路径与传导路径。

结合紫竹国家高新技术产业开发区研发二期的转型升级，探索重点园区如何挖掘自身潜力与优势、转变发展理念、落实实施路径，打造智慧资源集聚、双创活动多元、产学研城功能融合、兼具滨江生态人文特色的新型产业社区。

其创新转型路径研究将形成先期探索经验，成为产业园区向产业社区转型的范本。

Around the construction of Shanghai Science and Technology Innovation Center, this paper explores how urban planningcan serve and lead the upgrading and development of traditional industrial parks to comprehensive industrial communities. Adhering to the concept of "seeking greater benefits through small renewal", we creatively propose to prepare a framework for the overall level of the industrial park and its surrounding areas, covering strategy to action, highlighting strategy, conductivity and implementation. By specifying the technical framework of "goal-strategy-plan-action", we strengthen core function guidance and multi-dimensional space adaptation, and establish a transformation path. Based on the transformation and upgrading of R&D Base (II phase) of Zizhu National High-tech Industrial Development Park, this paper explores how key parks can tap their own potential and advantages, change their development concept, conduct the implementation path, and build a new industrial community with the characteristics of waterfront ecological humanity, which is characterized by smart resource concentration, multiple innovation and entrepreneurship activities. The research on its innovation transformation concept and implementation path will form an early exploration experience and become a model for the transformation of industrial parks to industrial communities.产业社区；转型战略；实施路径；国家高新技术产业开发区industrial community; transformation strategy; implementation path; national high-tech industrial development park文章编号 1673-8985（2022）06-0140-06 中图分类号 TU984 文献标志码 A DOI 10.11982/j.supr.20220619摘要Abstract 关键词Key words 作者简介袁芯上海市城市规划设计研究院总规分院工程师，硕士，****************0 引言《上海市城市总体规划（2017—2035年）》（以下简称“上海2035”）提出聚焦具有全球影响力的科技创新中心建设，加快建立以科技创新与战略性新兴产业引领、现代服务业为主体、先进制造业为支撑的新型产业体系，构建“产业基地—产业社区—零星工业地块”的产业空间布局体系。

中央引导地方科技发展的专业英文表达

中央引导地方科技发展的专业英文表达Central Guiding Local Technology DevelopmentIntroductionIn the era of rapid technological advancement, it is essential for central governments to play a significant role in guiding and promoting technological development at the local level. This essay aims to discuss the importance of central guidance in local technology development and explore various strategies that central governments can employ to support and facilitate technological innovation in different regions.The Importance of Central Guidance1. Coordination and CooperationCentral governments have the authority and resources to coordinate and facilitate cooperation among different local governments, research institutions, and industries. By acting as a central hub, they can bring together various stakeholders to work towards common technological goals, promote collaboration, and avoid duplication of efforts.2. Policy Formulation and ImplementationCentral governments are responsible for formulating and implementing policies that can create a conducive environment for technological development. These policies include funding opportunities, tax incentives, regulatory frameworks, and infrastructure development. By aligning policies at all levels of government, central governments can ensure a coherent and supportive environment for local technology development.3. Resource AllocationCentral governments play a crucial role in allocating resources for research and development activities in different regions. By providing funding, grants, and subsidies, they can support local initiatives and projects that have the potential to contribute to technological innovation and economic growth.Strategies for Central Guidance1. Establishing Technology HubsCentral governments can create technology hubs or innovation clusters in different regions to foster collaboration and knowledge sharing among local stakeholders. These hubs can serve as platforms for networking, research, and investment, attracting talent and resources to the region.2. Investing in InfrastructureCentral governments can invest in infrastructure projects such as research facilities, laboratories, and communication networks to support technological development in local areas. By ensuring access to essential resources and tools, they can enable local communities to engage in innovation and entrepreneurship effectively.3. Providing Financial SupportCentral governments can provide financial support to local research institutions, startups, and businesses through grants, loans, and venture capital funds. By investing in local talent and ideas, they can stimulate economic growth, create job opportunities, and promote sustainable development.4. Facilitating Technology TransferCentral governments can facilitate technology transfer by encouraging collaboration between universities, research institutions, and industries. By promoting knowledge exchange and technology diffusion, they can enhance the competitiveness of local businesses and accelerate innovation in different sectors.ConclusionIn conclusion, central guidance is vital for promoting technological development at the local level. By coordinatingefforts, formulating policies, allocating resources, and implementing strategies, central governments can support and facilitate innovation in various regions. By working together, we can harness the power of technology to address global challenges, improve quality of life, and build a more prosperous future for all.。

高中英语作文科学探究与创新精神

高中英语作文科学探究与创新精神Title: The Importance of Scientific Inquiry and InnovationIn the rapidly advancing era, the integration of scientific inquiry and innovation has become an essential driving force propelling societal progress.The contemporary world is replete with examples where scientific inquiry has led to groundbreaking innovations, transforming the way we live, work, and communicate.Scientific inquiry, at its core, is a systematic and organized approach to acquiring knowledge by means of observation, experimentation, and analysis.It encourages critical thinking and the development of problem-solving skills.Scientific inquiry fosters a deep understanding of the natural world, enabling us to comprehend and predict phenomena, which in turn empowers us to shape our environment for the better.Innovation, on the other hand, is the application of creative ideas to develop new products, services, or processes.It is often a result of scientific inquiry, as it builds upon the existing knowledge base to offer novel solutions to real-world problems.The innovation process requires a mindset that is open to risks, embraces failures as learning opportunities, and is persistently driven to improve and adapt.The synergy between scientific inquiry and innovation is pivotal in fostering an environment conducive to progress.As we delve deeper into the realms of science and technology, we unlock new possibilities thatwere previously uncharted.This interplay not only leads to economic growth through the development of new industries and job opportunities but also enhances our ability to address pressing global challenges, such as climate change, energy scarcity, and pandemics.Moreover, scientific inquiry and innovation are vital to nurturing a generation of young cating students about the scientific method and encouraging them to engage in hands-on experiments and projects sparks their curiosity and instills in them a lifelong love for learning.It also cultivates innovation-mindedness, which is critical for the future workforce.In conclusion, scientific inquiry and innovation are inseparable partners driving the engine of progress.They are not merely academic pursuits but are deeply connected to the betterment of society.Encouraging and investing in these areas is key to unlocking a future filled with possibilities and promise.As we continue to explore the unknowns of science and harness the power of innovation, we shape a world that is more sustainable, efficient, and full of hope.。

Environmental Innovation

•
Own compilation. Data Source: Esty et al. (2005)
Eco-innovations are Policy-driven
M. Jä nicke 2007
(3)
Environmental Governance and Competitiveness 2004
- Correlation w. Growth Competitivenes Index (+GDP/cap.) -
• Environmental Governance (x): 0.80 (0.78) • Private sector environmental responsiveness: 0.83 (0.76) • Participation in international environm. collaborative efforts: 0.87 (0.83)
(1)
Definition: Eco-efficient Innovation and Ecological Modernisation
• Eco-efficient innovation is the creation and diffusion of novel competitive goods, processes and services designed to preserve or improve the environment with a life-cycle minimal use of natural resources (see also EuropeINNOVA/EC, 2006). • Eco-efficient innovation is a synonym for „ecological modernisation“. • „Environmental innovation“ is the broader term including both, eco-efficient and „end-ofpipe“ technology.

新发展理念英语作文初三

In recent years,the concept of innovative development has been widely recognized and implemented in various fields.This essay aims to explore the significance of the new development concept and its application in the context of todays society,particularly focusing on the perspective of a junior high school student.Title:Embracing the New Development Concept in Junior High SchoolIntroduction:The era we live in is characterized by rapid changes and advancements.As a junior high school student,I have observed the impact of the new development concept on education, technology,and social progress.This essay will delve into the essence of this concept and how it shapes our future.The Core of the New Development Concept:The new development concept emphasizes innovation,coordination,green development, openness,and sharing.It is a holistic approach that seeks to balance economic growth with social equity and environmental sustainability.Innovation:Innovation is at the heart of the new development concept.It is the driving force behind technological advancements and economic prosperity.As students,we are encouraged to think creatively and solve problems in novel ways.Schools are integrating more handson learning experiences and fostering an environment that values curiosity and experimentation.Coordination:Coordination is crucial for harmonious development.It involves aligning the efforts of various sectors to achieve common goals.In the educational context,this means ensuring that all students have equal opportunities to learn and grow.It also means that schools work closely with parents and the community to support students wellbeing.Green Development:The new development concept underscores the importance of environmental protection. Green development is not just about reducing pollution but also about creating a sustainable future.Schools are teaching students about the importance of recycling, conserving energy,and protecting biodiversity.This awareness is crucial for the next generation to become responsible stewards of the planet.Openness:Openness is about embracing diversity and learning from different cultures.In anincreasingly globalized world,it is essential for students to understand and respect different perspectives.Schools are promoting international exchanges and encouraging students to learn foreign languages to foster a more inclusive and interconnected society.Sharing:Sharing is about ensuring that the benefits of development are accessible to all.It is about creating a society where everyone has the opportunity to succeed.In education,this means providing resources and support to students from all backgrounds,ensuring that no one is left behind.Conclusion:The new development concept is not just a theoretical framework it is a practical guide for shaping a better future.As junior high school students,we are at the forefront of this change.By embracing these principles,we can contribute to a society that is more innovative,harmonious,sustainable,inclusive,and equitable.It is our responsibility to carry these values forward and make a positive impact on the world.。

国家合作抗疫英语作文简单

In the face of the global pandemic, the importance of international cooperation has been highlighted like never before. Countries around the world have come to realize that no single nation can combat the virus alone. The following are some key points that illustrate the significance of national cooperation in fighting the pandemic:1. Sharing of Information and Resources: The first step in combating the virus is the sharing of accurate and timely information among nations. This includes data on the viruss spread, its symptoms, and the most effective treatments.2. Joint Research and Development: International collaboration in research and development is crucial for creating vaccines and treatments. Scientists from different countries can pool their knowledge and resources to expedite the process of finding a cure.3. Supply Chain Coordination: The pandemic has disrupted global supply chains, making it difficult for countries to access essential medical supplies. By working together, nations can ensure the equitable distribution of these resources.4. Economic Support: The economic impact of the pandemic has been felt worldwide. Countries can support each other by providing financial aid or trade concessions to help mitigate the economic downturn.5. Public Health Infrastructure: Nations can learn from each others experiences and best practices in building robust public health systems. This includes the establishment of efficient testing, contact tracing, and quarantine protocols.6. Policy Coordination: Coordinating policies on travel restrictions, border controls, and quarantine measures can help prevent the spread of the virus across borders.7. Education and Awareness: Countries can work together to create public awareness campaigns about the importance of preventive measures such as handwashing, social distancing, and wearing masks.8. Longterm Preparedness: The pandemic has shown the need for longterm preparedness for such crises. Nations can collaborate on developing strategies and infrastructure to be better prepared for future health emergencies.In conclusion, national cooperation is not just a choice but a necessity in our interconnected world. By working together, we can overcome the challenges posed by the pandemic and build a safer and healthier global community.。

外文翻译--产业集群的发展和公共政策

本科毕业设计（论文）外文翻译外文题目：The development of industrial clusters and public policy出处：ENTREPRENEURSHIP & REGIONAL DEVELOPMENT, 18,NOVEMBER (2006), 525–542作者：FRANK MCDONALD, DIMITRIOS TSAGDIS and QIHAI HUANGAbstractThis paper assesses the relationships between public policy and the development of industrial clusters. A conceptual model of the relationship between public policies and the development of industrial clusters is developed and tested using data from 43 European industrial clusters. The results indicate that most government policies have no significant impact on the growth of industrial clusters or for the development of co-operation within industrial clusters. There is limited evidence that packages of government policies that are specifically geared towards improving the local asset base are effective in overcoming obstacles to growth of industrial clusters. However, when age is used as a control variable the weak relationship between policy packages and growth of industrial clusters disappear. The results indicate that individual and packages of public policies are not strongly connected to either high levels of co-operation, or high growth in industrial clusters. Moreover, no clear evidence was found that high levels of co-operation were associated with growth in industrial districts. In the light of the failure to find clear-cut associations between public policies and the development of industrial clusters the paper outlines a research agenda to help to increase our understanding of these issues.一、Characteristics of industrial clustersThe benefits of industrial clusters arise from technological externalities, the ability to increase innovation and learning, and the potential to enhance the flexibility and effectiveness of production and distribution systems. Brown and McNaughton (2002), and Paniccia (2002) provide extensive reviews of the literature on these characteristics. The importance of industrial clusters for the international competitiveness of firms has also been examined (Enright 1998, Porter 2000) including the potential benefits for international entrepreneurship by SMEs (Enright and Ffowcs-Williams 2000, Brown and Bell 2001). A variety of names have been given to geographical concentrations of firms and supporting agencies including, industrial clusters, industrial districts, production clusters, technopoles, business agglomerations, and spatially concentrated high-technology clusters. This paper uses the term industrial cluster to cover all types of geographical concentrations of firms and supporting agencies.As indicated in the other papers in this special issue there are many different ways to construct typologies of geographical concentrations of firms and supporting agencies. However, there are no generally agreed boundaries on the organization, spatial, competition/co-operation structure, technological, industrial structure, and institutional characteristics of industrial clusters. Some researchers stress the importance of local learning as the core of what defines a cluster (Asheim 1996, Amin and Wilkinson 1999, Cooke 2002a), while others regard effective links between market processes and institutional and cultural factors (social capital) as being central (Dei Ottati 1994,2002, Bellandi 2002, Sforzi 2002, O’Gorman and Kautonen 2004). The variety of organization and competitive/co-operation structures evident in industrial clusters is also used to construct a multitude of typologies (Markusen 1996, Paniccia 1998).Although there are differences in definitions and approaches to the study of industrial clusters there is a remarkable degree of agreement that geographical concentration of firms and supporting agencies can create and develop competitiveadvantages for firms located in industrial clusters (Porter 1990, 1998, Enright 1998, Weaver and Stam 1999, Cooke 2002a, Cooke and Huggins 2002, Dei Ottati 2002, Garofoli 2002, Sforzi 2002). There are differences of opinion about the emphasis given to the main sources of such benefits and about over simplistic analysis, in some parts of the literature, on the key characteristics of industrial clusters (Martin and Sunley 2003). However, the advantages of developing industrial clusters are widely shared across the research community. The main identified generic requirement for successful clusters is that firms develop strong co-operative networks with other firms and supporting agencies within a cluster (Porter 1994, 2000, Swann et al. 1998, Dei Ottai 2002, Garofoli 2002, Lundequist and Power 2002). There are considerable differences of emphasis on the main characteristics of networks in terms of the importance of competition versus co-operation (Dei Ottati 1994, Newlands 2003), the relevance of proximity to key actors in the industrial cluster (Lublinski 2003), the main actors and their relationships in networks (Chell and Baines 2000, Vanhaverbike 2001, Carbonara 2002, Julien et al. 2004) and the importance of underpinning by socio-economic networks (Huggins 2000, Dei Ottati 2002, Drakeopoulou and Patra 2002). The relative importance of international, national, and local links in networks in industrial clusters is also becoming an important factor of research and also for policy (Simmie and Sennett 1999, Hendry et al. 2000, Simmie 2003, Britton 2004, Mackinnon et al. 2004). The international relocation of activities from industrial clusters is also an important area of research and policy advice (see Biggiero in this issue). The possibilities of developing learning and relationship capital (within industrial clusters) as a means of protecting industrial clusters from loss of business in increasingly global supply chains is also a strong focus of research (see Molina-Morales and Martinez-Fernandez; and Sammarra and Belussi in this issue).Notwithstanding the wide variety of views and approaches taken in research on industrial clusters, the need to develop deep networks is accepted by many researchers in this area. Deep networks have a large number of connections both with other firms in the industrial cluster and with supporting agencies. These networks are primarily used to gather process and disseminate information and knowledge that is useful to develop the firms and supporting structures within industrial clusters. Most of theliterature also accepts that the more extensive are networks the more industrial clusters will deliver competitive advantages. Extensive networks are based on high and well developed flows of goods, services, and information in local supply chains that deliver internal and external economies of scale and flexibility in production and distribution. Both deep and extensive networks are deemed to be helpful for boosting organization learning and innovation.A large body of case study evidence has been produced that sustains the view that business networking in industrial clusters has very diverse characteristics, but most of this literature supports the opinion that deep and extensive networks provide beneficial outcomes for firms located in industrial clusters. Policies that enhance the quantity and quality of the local asset base are also commonly cited as being important for the development of industrial clusters. This body of work includes case studies on famous US clusters (Bania and Eberts 1993, Saxenian 1994, Kenney and Burg 1999), creative industries (Kra¨ tke 2002, Turok 2003), and high-tech industries (Department of Trade Industry [DTI] 1999, Weaver and Stam 1999, Cooke and Huggins 2002). Some of the cases study evidence involved comparative international studies (Swann et al. 1998, Hendry et al. 2000). Studies have also been carried out on the importance of socio-economic networks and institutional frameworks (Dei Ottati 2002, Johannisson et al. 2002, Sforzi 2002).A few large-scale studies have been carried out to assess the main characteristics of industrial clusters and to investigate links to performance (DTI 2001, Harvard Business School 2002). These studies support the view that industrial clusters have a wide variety of different characteristics, but that deep and extensive networks are normally associated with good performance. A survey by the European Commission of the major studies on industrial clusters in Europe discovered that 13 countries had conducted large-scale surveys (European Commission 2002). The review of European surveys revealed a large spread of characteristics in terms of sector/industry, the geographical extent of industrial clusters, number and type of firms that belong to industrial clusters, and the extent and character of networks within clusters.The survey by the European Commission also investigated 34 industrial clusters in Western Europe and concluded (like most of European studies in this area) thatclusters with deep and extensive networks were a major requirement for successful performance. These studies also support the view that improvements in the local asset base help the growth of industrial clusters.摘要本文讲述了公共政策与产业集群发展的关系。

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• Agreed to implement a programme : Enhanced coordination and development of enteric disease research in New Zealand .
• Involve people from primary industries, research organisations, policy and regulatory agencies, research funding agencies.
– Facilitated establishment of PulseNet Aotearoa New Zealand, a database for typing and tracking zoonotic microorganisms.
– Established a website:
– Goodwill and support of key organisations – Shared commitment – Agreed goals and priorities (‘singing to the same song
sheet’) – Industry and government funding – Drive of key individuals (‘champions’)
Coordination and Development of Enteric Zoonotic Disease Research in New Zealandmittee:
– Independent chair
– Members drawn from stakeholder groups:
• Isolation of thermotolerant Campylobacter – review and methods for New
Zealand laboratories.
September 2019.
• Subtyping of zoonotic pathogens – the role of method standardisation, and
• Preliminary relative risk assessment for Campylobacter in New Zealand. July 2019.
Coordination and Development of Enteric Zoonotic Disease Research in New Zealand.
Coordination and Development of Enteric Zoonotic Disease Research in
New Zealand.
by
Don McGregor*
*Inaugural Chair, Enteric Zoonotic Disease Research Coordination Steering Committee; *Formerly Chief Scientist, Ministry of Research, Science and Technology; *Emeritus Professor of Zoology, University of Otago
electronic databases in the reduction of the infectious disease burden in
New Zealand.
August 2019.
• National Campylobacter qualitative risk assessment (QRA) modeling study of sources, transmission routes and exposures. November 2019.
New Zealand.
Research commissioned and coordinated by the Steering Committee has lead to:
• Better understanding of the ecology and transmission of Campylobacter.
– Develop, implement and review conceptual models for managing Campylobacter and other zoonotic organisms.
Coordination and Development of Enteric Zoonotic Disease Research in
Coordination and Development of Enteric Zoonotic Disease Research in New Zealand.
Outputs of work sponsored by the Steering Committee include:
• A survey of phenotypic and genotypic methods used to identify and differentiate thermotolerant Campylobacter species and strains. June 2019.
Coordination and Development of Enteric Zoonotic Disease Research in New Zealand.
BEGINNINGS:
• 2000 – Meeting convened by Dr Alexander Kouzminov (MoH), attended by people from government departments, CRIs, universities.
Conclusion:
The successful coordination of research, and its alignment with the needs of policy and regulatory agencies, and with industry, has been
achieved through:
• MISSION:
– “To reduce the disease burden of enteric zoonoses in New Zealand”
• OBJECTIVES:
– Promote and advance enteric disease research in New Zealand.
– Encourage and facilitate cooperation, collaboration and sharing of information by researchers and research organisations.
– Assist with and facilitate transfer of results of research to end-users and other stakeholders.
• Development of quantitative risk assessment models for Campylobacter.
• Development of guidelines for more effective interventions to prevent water- and food-borne diseases.
– Played important roles in facilitating Cross Department Research Pool research on Campylobacter.
– Facilitated establishment of a National Reference Culture Collection for zoonotic microorganisms.
Objectives:
– Advise the Steering Committee on risk management and methodology priorities.
– Develop and promulgate standardized methodologies for research on enteric zoonotic diseases.
• Poultry industry • Dairy industry • MoH • MAF • NZFSA • CRIs • Universities
Coordination and Development of Enteric Zoonotic Disease Research in
New Zealand.
END
Coordination and Development of Enteric Zoonotic Disease Research in
New Zealand.
The Steering Committee has:
– Transmitted its research priorities to the Foundation for Research, Science and Technology.
– Facilitate and coordinate involvement of users, funders and providers of research.
– Set research goals and priorities.
– Advise government agencies and industry on research priorities and research gaps.
Coordination and Development of Enteric Zoonotic Disease Research in New Zealand.