Innovation strategies for a Global Economy: Development, Implementation, Measurement and Management by Fred Gault
Bibliographic record
Abstract
In the rarefied world of innovation indicators there are a few individuals whose every new word on the subject generates immediate interest. Certainly Fred Gault ranks highly among them. Few have more practical knowledge and experience of how these indicators operate within systems of national accounts, and very few share his depth of diplomatic experience over many years in guiding the development and application of international statistical definitions and standards in the OECD and elsewhere. Not surprisingly, this book is very much written from an insider perspective and with practical intent. One of its main objectives is to guide the design and deployment of indicator regimes in newly industrializing countries. However, readers will encounter none of the apologetics, special pleading or credit-taking that too often mars the insider view. This is a refreshingly candid and critical examination of the origins, purposes, strengths, weaknesses, and implications of statistical regimes for assessing national innovation performance. It is very much in line with active debates in the OECD and the European Commission on the future directions of innovation policy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".