Left on the shelf: Explaining the failure of public inquiry recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it