Policy-Makers, Policy-Takers and Policy Tools: Dealing with Behaviourial Issues in Policy Design
Bibliographic record
Abstract
Getting the incentives (and disincentives) right in order to ensure proper levels of compliance with government initiatives is a vital assumption of much of the writings on policy design. The assumption, however, overlooks or underestimates other critical factors that affect compliance. This includes policy-makers’ behaviour in the social and political construction of policy targets and it also minimizes the complex objective and subjective conditions that affect the target population’s attitudes and behaviours in relation to policy-maker aims and goals. The notion of policy-takers as static targets who passively receive policies without trying to evade or even profit from them is as misguided as the assumption that policy-makers only consider evidence on policy tools’ effectiveness before selecting them. This article highlights the critical issue surrounding the choice and workings of policy tools and introduces the papers in this special issue. It indicates how they contribute to filling the gaps in our existing understanding of policy tools and advance our understanding of both policy-maker and policy-taker behaviour.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.208 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.013 | 0.108 |
| Scholarly communication | 0.053 | 0.045 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.023 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 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".