Effectively financing private sector innovation? Toward a conceptual policy framework
Bibliographic record
Abstract
Abstract Our understanding of innovation policies has been enhanced. However, there is still a gap in conceptualizing the effectiveness of one of innovation policy’s most important tools: financial incentives (FIs). Scholars developed an understanding of the effectiveness of direct versus indirect FIs, but there is no clear theoretical framework that delineates what kind of financial instruments impact what kind of innovation under what conditions. This paper analyzes the different working and operational logic of the wide array of employed FI worldwide to develop what is, to the best of our knowledge, the first conceptual framework discerning what financial tools fit what aims and contexts. This framework allows the development of testable hypotheses as well as the development of incentives tailored differently for different national innovation missions and market structures, suggesting that the growing reliance among OECD countries on indirect FIs in the form of tax incentives is less then optimal.
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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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".