The politics of partial success: fostering innovation in innovation policy in an era of heightened public scrutiny
Bibliographic record
Abstract
Policymakers have adapted to the challenge of rapid, innovation-based competition by using ‘Schumpeterian development agencies’ (SDAs) to foster continuous, radical experimentation. These peripheral agencies developed new policy instruments and business models that would transform their national economies when scaled by mainstream actors. In this article, we argue that this model is less likely to succeed in an increasingly politicized environment. The growing salience of innovation has instead led to the ‘politics of partial success’, sharpening the trade-offs between policy experimentation and implementation. We develop these arguments by first reviewing the history of two successful SDAs, the Finnish National Fund for Research and Development and the Israeli Office of the Chief Scientist, and then more deeply examining three newly established innovation agencies, Singapore’s Agency for Science, Technology and Research (A*STAR), the Swedish Governmental Agency for Innovative Systems (VINNOVA), and Ireland’s Policy Advisory Board for Enterprise and Science (Forfás).
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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 | 0.042 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.055 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".