Left on the shelf: Explaining the failure of public inquiry recommendations
Bibliographic record
Abstract
Abstract Public inquiries remain the pre‐eminent mechanism for lesson‐learning after high‐profile failures. However, a regular complaint is that their recommendations get ‘shelved’. In political science, the most common explanation for this lack of implementation tells us that elites mobilize bias in order to undermine inquiry lesson‐learning. This article tests this thesis via an international comparison of inquiries in Australia, Canada, New Zealand and the UK. A series of alternative explanations for shelving emerge, which tell us that inquiry recommendations do not get implemented when: they do not respect the realities of policy transfer; they are triaged into policy refinement mechanisms; and they arrive at the ‘street level’ without consideration of local delivery capacities. These explanations tell us that the mobilization of bias thesis needs to be reworked in relation to public inquiries so that it better recognizes the complex reality of public policy in the modern state.
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 | 0.048 | 0.186 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".