The Knowns and Unknowns of Policy Instrument Analysis: Policy Tools and the Current Research Agenda on Policy Mixes
Bibliographic record
Abstract
Policies are made and pursue their goals through policy instruments. Furthermore, policy instruments have become a relevant topic in many policy fields due to their theoretical and empirical relevance. The study of this field dates back to Lowi and others who developed many typologies and theories in classic works by authors such as Hood, Salamon, Linder and Peters, Peters and van Nispen, Schneider and Ingram, Lascomes and Le Galès, among others. This is important work that is linked closely to current research on policy design but, despite much effort, many fundamental issues remain unknown or understudied with respect to the topic. It is time to take inventory of the knowns and unknowns about policy tools. The current article examines four clusters of basic issues in the field which require further research.
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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 | 0.063 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.005 | 0.073 |
| Scholarly communication | 0.036 | 0.081 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".