Unpacking policy portfolios: primary and secondary aspects of tool use in policy mixes
Bibliographic record
Abstract
A recent resurgence of interest in policy design has fostered renewed efforts to better understand how specific combinations of policy tools arise and shape policy outcomes. However, to date, these efforts have been stymied by under-theorization of the dif- ferent purposes to which tools are directed in policy mixes and a corresponding failure to acknowledge both these in conceptual work on the subject and in policy practice. Existing frameworks do not adequately recognize the complexity of contemporary policy tool mixes, especially their hybrid and multilayered features, and how procedural and substantial tools operate and interact together in priority and supportive roles. To close this gap, we propose a revised tool framework that distinguishes between first and second-order aspects of instruments used in policy mixes and highlights the particular salience of procedural tools within them.
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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 | 0.036 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.028 | 0.042 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.005 |
| 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".