Beyond evidentiary uncertainty: mitigating political risks in policy designs
Bibliographic record
Abstract
Policy designs occur within complex environments where multiple actors, conflicting goals and multi-layered opportunity-cost calculations must be accounted for before, during and after a decision is made. Here we propose a useful framework to employ in managing the political and administrative uncertainties accompanying these activities and identifying what kind of design is desirable in any given circumstance. This brings together two dimensions or attributes of the decisional context: the administrative capacity of the organization and the political desirability of a specific choice and especially the distribution of blame and credit that any choice is likely to carry. The article outlines each of these dimensions and how the risks and uncertainties attached to each can be mitigated through the design process.
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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 | 0.149 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".