Calibration and specification in policy practice: Micro-dimensions of policy design
Bibliographic record
Abstract
Three aspects of policy success -programme implementation, policy solution feasibility and political legitimacy and support -need to be at the front of mind when policies are formulated.Many uncertainties endemic to policy-making surround these issues and present considerable public management challenges.Many of these problems, however, are linked to the poor conceptualization and understanding of policy content on the part of policy-makers, something for which policy scholars must share some blame.This is especially true with respect to the existing literature on the micro-level aspects of policies; the level at which goals and policy instruments are concretely implemented in the form of specific policy targets and tool calibrations.While these latter subjects have been examined in the past by luminaries such as Eleanor Ostrom, Guy Peters, Peter Hall and Lester Salamon, their insights into this level of policy-making have been glossed over in the mainstream policy sciences and the significance of their work for real-world policy analysis insufficiently appreciated.This article sets out a framework of policy calibrations and specifications that reconciles and incorporates these insights in order to enhance the chances of policy success through improved policy design.
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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 | 0.213 | 0.285 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.006 | 0.060 |
| Scholarly communication | 0.031 | 0.027 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".