The lessons of failure: learning and blame avoidance in public policy-making
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
Recent studies by Hood have underscored the significance of the desire of decision-makers to avoid blame for poor policy initiatives, highlighting the importance to policy-making of learning about how best to avoid policy failure. This article examines several different concepts of policy failure in the literature on the subject, such as policy accidents, errors, mistakes, and anomalies, along with recent work by McConnell and his colleagues on the general types and sources of such failures. The article distinguishes between ‘thin’ (technical-strategic) and ‘thick’ (political-experiential) policy learning and links them to McConnell’s three categories of political, programme, and process failures. The analysis points to the significant and underappreciated roles played by process and political problems in the analysis of policy failure and the need to draw lessons in these areas as well as in more technically oriented programme-related ones if the prospects of policy success are to be enhanced.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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