What gets done and why: Implementing the recommendations of public inquiries
Bibliographic record
Abstract
Abstract: Public inquiries are often the instrument of choice when governments decide to re‐think their approach to large issues, yet there has been little empirical research on how effective they are. This article is an evidence‐based look at what affects the implementation of recommendations made by public inquiries. It considers eleven inquiries, examining how they operated, their political and administrative setting, and what action was taken on the recommendations. The central hypothesis is that governments do implement the recommendations of public inquiries under certain conditions. Such implementation extends not only to technical, incremental recommendations but also to recommendations involving systemic changes. The findings point to the role of judges who head and preside over inquiries as policy influencers. Often buffered by inquiry counsel or policy staff, judges may test potential recommendations with governments and other interested parties. The impact of inquiry hearings suggests that public inquiries do not necessarily serve a government's wishes to delay action. If inquiry hearings are the top item in the news, it is hard to see how that furthers a government agenda to bury the issues. Sommaire: Les enquêtes publiques sont souvent l'instrument de choix auquel ont recours les gouvernements lorsqu'ils décident de repenser leur manière d'envisager les grandes questions, or peu de recherches empiriques ont été entreprises pour déterminer de leur efficacité. Le présent article est un examen fondé sur les données probantes de ce qui influe sur la mise en œuvre des recommandations résultant d'enquêtes publiques. Il passe en revue onze enquêtes publiques, examinant comment elles ont fonctionné, quel était leur cadre politique et administratif, et quelles mesures ont été prises à propos des recommandations. L'hypothèse principale est que les gouvernements mettent effectivement en œuvre les recommandations des enquêtes publiques dans certaines conditions. Une telle mise en œuvre couvre non seulement les recommandations techniques croissantes, mais aussi les recommandations concernant les changements systémiques. Les résultats attirent l'attention sur le rôle joué par les juges qui dirigent les enquêtes par l'influence qu'ils exercent sur les politiques. Les juges, souvent utilisés comme tampons par les avocats des enquête publiques ou le personnel chargé des politiques, peuvent tester les recommandations potentielles auprès des gouvernements et autres parties intéressées. L'impact des audiences des enquêtes publiques laisse entendre que les audiences publiques ne servent pas nécessairement à retarder les mesures à prendre comme pourrait le souhaiter le gouvernement. Si les audiences d'une enquête publique font la une des médias, il est difficile de voir comment un programme gouvernemental peut progresser en étouffant les questions.
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 | 0.182 | 0.426 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".