Avoiding a Panglossian Policy Science: The Need to Deal with the Darkside of Policy-Maker and Policy-Taker Behaviour
Bibliographic record
Abstract
Current work on policy design typically views policy-making as an activity on the part of well-intentioned governments desiring to serve the public interest by marshaling accurate evidence in a dispassionate, technical way in the attempt to address and resolve public problems. Even those studies which do note the political and power-based nature of many aspects and instances of policy-making still hold out hope that these “distortions” can be corrected and effective policy solutions emerge from policy deliberations. While this optimism is laudable, such thinking does a disservice to policy design and policy studies by failing to address head-on the possibilities, often observed in policy-making practice, that policy-makers may be driven by malicious or venal motivations rather than socially beneficial or disinterested ones and that policy targets also have proclivities and tendencies towards activities such as gaming, free-ridership and rent-seeking that must be curbed if even well-intentioned policies are to achieve their aims. This paper addresses both these issues and the state of the policy design literature on the causes, consequences and correctives for such behaviour. It proposes a new research agenda dealing with this “dark side” of policy behaviour and the manner in which policy designs can include procedural policy tools in order to deal with these problems.
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 | 0.253 | 0.265 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.016 | 0.206 |
| Scholarly communication | 0.047 | 0.064 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.030 | 0.050 |
| 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".