Bibliographic record
Abstract
This article discusses the concept of success in relation to policy experimentation, a type of policy innovation. Despite a growing literature on policy experiments, few studies define or explore what constitutes a successful experiment. This is important given the potential of experiments for policy learning and change, and also so that experiments can be evaluated and lessons learned across jurisdictions by academics and practitioners. The article develops a concept of success that is rooted in policy learning. Based on an empirical study of policy experimentation in Canadian federal cultural, heritage and sport policy, the article outlines four key ‘success criteria’: basic elements of an experiment; leadership and resources; procedural elements; and evaluation. It argues that experiments need to be evaluated not merely on outcomes, and that they should ultimately aim to encourage reflexive learning, characterised by dialogue and deliberation.
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 | 0.176 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.051 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".