Procedural policy tools in theory and practice
Bibliographic record
Abstract
ABSTRACT Policy tools are a critical part of policy-making, providing the ‘means’ by which policy ‘ends’ are achieved. Knowledge of their different origin, nature and capabilities is vital for understanding policy formulation and decision-making, and they have been the subject of inquiry in many policy-related disciplines and sector-specific studies. Yet many crucial aspects of policy tools remain unexplored. Existing studies on policy tools used in policy formulation tend to focus on ‘substantive’ tools – those used to directly affect policy outcomes such as regulation or subsidies – and largely neglect ‘procedural’ tools used to indirectly but significantly affect policy processes and outcomes. A key aim of this special issue is to fill this knowledge gap in the field. This article introduces the issue by establishing that procedural tools play a more determining role in public policy-making than is generally acknowledged and deserve a more systematic inquiry into their workings, their impact on the policy process and the organization and delivery of public and private goods and services.
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 | 0.047 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.081 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".