Dealing with the Dark Side of Policy-Making: Managing Behavioural Risk and Volatility in Policy Designs
Bibliographic record
Abstract
Policy studies to date have focused almost exclusively on the “good” side of policy formulation, that is, dealing with concerns around ensuring that knowledge is marshalled towards developing the best feasible policy under the assumption of well-intentioned governments and accommodating policy targets. This work has not carefully examined nor allowed for the possibility that government intentions may not be solely oriented towards the creation of public value or that policy targets may indulge in various forms of “misconduct” – from fraud to gamesmanship – which undermine government intentions. Although self-interested, corrupt and other similar kinds of policy-making have been the subject of many studies in administrative and regulatory law, this work has generally been ignored or paid only lip service by policy studies. This is changing, however, as the question of the behaviour of policy targets in particular has increasingly become a source of interest among policy scholars. This article reviews these developments and behaviours in order to aid the process of improving policy designs to deal with this “dark side” of policy-making.
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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 | 0.217 | 0.377 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".