The criteria for effective policy design: character and context in policy instrument choice
Bibliographic record
Abstract
Recent studies of policy design have grappled with such issues as policy tool use, overcoming historical policy legacies, the nature of policy mixes and issues around policy formulation and the nature of ‘design’ and ‘designing’ in policy-making. These studies have begun to establish insights into what makes a policy design ‘effective’ or likely to succeed in being adopted or implemented or both. This paper draws lessons from both the ‘old’ and the ‘new’ design work to establish several basic criteria for effective design and designing. As the review of the literature shows, the kinds of lessons that can be drawn from these studies fall into two categories: those dealing with matching design activity to the context of policy-making and those which focus on the character of the tools deployed in a design. The paper sets out both these elements and shows how they can be combined to generate lessons, insights and practices for both policy scholars and practitioners alike.
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 | 0.180 | 0.273 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.010 | 0.062 |
| Scholarly communication | 0.029 | 0.022 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.011 | 0.008 |
| 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".