Policy innovation lab scholarship: past, present, and the future – Introduction to the special issue on policy innovation labs
Bibliographic record
Abstract
The past decade has seen a rapid rise in the number of policy innovation labs (PILs). PILs that are found both inside and outside of government address a wide range of social issues. Many PILs share a few distinct common characteristics: a commitment to the design-thinking methodology, a focus on applying experimental approaches to testing and measuring the efficacy of comprehensive public policy and intervention program prototypes, and the use of user-centric techniques to stakeholders in the design process. In this introduction to the special issue on PILs, we begin by taking stock of the policy lab literature published to date by providing an overview of 70 related publications (peer review articles, book chapters, theses, reports, and catalogs) and the extent that they engage the policy literature. This review demonstrates the underexplored practitioner perspective, which serves as the theme for this special issue. Next, the six articles that comprise this special issue are introduced. They are written from a practitioner perspective and include contributions from Brazil, Canada, Finland, and the United Kingdom. Finally, suggestions for future research are highlighted, including the role of PILs in policy work, PILs as street-level policy entrepreneurship settings, and the need for more rigorous inferential methods.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".