National innovation and the academic research enterprise : public policy in global perspective
Bibliographic record
Abstract
This volume analyzes the impact of public policy on the knowledge economies and higher education systems of Organisation for Economic Co-operation and Development (OECD) countries, including Australia, Canada, Japan, Finland, Germany, the Netherlands, the United Kingdom, and the United States, as well as the overall European Union.<br/>Given that innovation is a national economy’s most valuable asset in today’s global marketplace, countries are investing more than ever in academic research, doctoral education, and the process of knowledge transfer. Policymakers now perceive the academic enterprise as a means of sustaining international competitiveness, and newly implemented national innovation policies represent a marked shift away from traditional science and technology policies.<br/>The contributors examine this new relationship between higher education and national systems of innovation, posing important questions: How is public policy affecting academic research? How are postsecondary institutions responding? The book concludes with a careful assessment of the effectiveness of current national innovation policy on higher education systems.<br/>Bolstered by the latest economic research on higher education, this book will be read by researchers and policymakers worldwide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".