Paper Prepared for the Workshop on Policy Failure
Bibliographic record
Abstract
A significant factor affecting policy failures and their management issues pertains to governmental and non-governmental “policy analytical capacity”. That is, governments require a reasonably high level of policy analytical capacity in order to perform the tasks associated with managing the policy process in order to avoid the most common sources of policy failures. Government require the ability to develop medium and long-term projections, proposals for, and evaluations of, future government activities and not simply react to short-term political, economic or other challenges and imperatives occurring in their policy environments if picy failures are to be avoided. Recent studies, however, suggest that the level of policy analytical capacity found in Canadian governments and non-governmental actors is low, contributing to a failure to effectively deal with many complex contemporary policy challenges. Introduction: Judging Policy Success and Failure Policies can succeed or fail in numerous ways. Sometimes an entire policy regime can fail, while more often specific programs within a policy field may be designated as successful or unsuccessful. And both policies and programs can succeed or fail either in substantive terms— that is, as objectively or perceived to be delivering or failing to deliver the goods—or in
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.194 | 0.034 |
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".