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Record W1540976619

Industry, Government Leaders Confront World's Innovation Needs

2004· article· en· W1540976619 on OpenAlexaboutno aff
Jacques Richardson

Bibliographic record

VenueResearch-Technology Management · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicUniversity-Industry-Government Innovation Models
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Investment (military)Political sciencePublic relationsPoliticsLaw
DOInot available

Abstract

fetched live from OpenAlex

Science policy-the ordered management of research-remains as hard to define as industrial policy. But research ministers and industry leaders from 30 countries have made a good stab at understanding the forces binding the processes of innovation with scientific advance. Their venue was the January Organization for Economic Cooperation and Development (OECD) meeting in Paris, where they explored Science, Technology and Innovation for the 21st Century. the chair was Australia's minister for science, Peter McGauran. a world of increasing opportunity and challenge, spending on RD business investment in research continued to be double that of government. Social Needs Weigh on Research These trends are continuing, since governments today are more harried by society-related programs than ever before. Official allocations to support research are thus often easy victims to outside pressures. Consider, for example, the extraordinary heat wave and drought that struck western Europe in summer 2003. sudden demand placed on public-health and water services in France, the area hardest hit, siphoned funds to these sectors from (among others) the research community's budget. One consequence, taking place at the very moment of the OECD science policy conference, was public protest that brought some 10,000 researchers from their labs to the Paris streets. Added to such episodic financial woes are more lingering problems that hinder the march of science. First among these the reluctance of young people to study science, math or engineering. Britain, France and Germany especially, university students face a hitherto virtually unknown threat of tuition fees. This combines with another imminent deterrent, the cross-border harmonization of university degrees-most likely along the lines of the North American model of bachelor's-master's-doctor's degrees. Many professors and students perceive such changes as not only a disruption of current university rhythms but a menace to national excellence in different disciplines. Then, once the young techie ready for professional life, cultural obstacles to job mobility take over. These mobstacles include * Reluctance to move far from home. * Coping with a foreign language or culture. * long tradition of staying with the same employer. * A profound aversion to skipping from university to industry to government, American-style. All of these hurdles blend into a negative synergism that curbs the creation and diffusion of knowledge-with concomitant impacts on innovative research, patent applications and economic access to intellectual property by other players, particularly the less industrialized countries. Meanwhile, the drain of the 1960s-1970s survives; it regularly affects some countries: Britain, Germany, Italy and France (see RTM Perspectives, March-April 2004, pp. 2-3), but especially those of Asia and Latin America. However, a few nations outside the U.S., Canada and Australia actually benefit from a brain gain, notably Sweden and New Zealand. new Irish Institute of Science has been remarkably effective, too, in persuading young native researchers to remain in the country's industries. In today's knowledge society we must discriminate among knowledge, inventiveness, and technology, emphasized Diana Bracco, CEO of Italy's Bracco Group. We need motivated young people capable of logical thinking. research community a meritocracy, the rewards of performance are based on achievement. Environment, Safety and Security There are other factors at play, such as environmental risks. President George W. Bush's science advisor and head of the Office of Science and Technology, John H. Marburger III, spelled some out: The most important thing in terms of pollution from energy use, he said, is how we transform energy from the basic fuels. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0190.017
Scholarly communication0.0290.017
Open science0.0010.010
Research integrity0.0220.017
Insufficient payload (model declined to judge)0.0160.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.300
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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