Puzzling Publics: The role of reflexive learning in universal pre-kindergarten (UPK) policy formulation in Canada and the US
Bibliographic record
Abstract
Building on theories of social learning and policy change, this article argues that reflexive learning provides a causal mechanism for how public engagement in policy formulation can trigger policy innovation. Reflexive learning is a mode of learning that takes place during policy formulation and is most likely to occur in policy areas marked by considerable uncertainty and complexity (low problem tractability) and the participation of a wide range of actors (low actor certification). We contend that reflexive learning processes can restructure policy problems and widen the menu of available policy options and prompt policy elites and citizens to collectively update their beliefs, resulting in policy innovation. We probe the plausibility of this mechanism of policy change through a comparative study of universal pre-kindergarten policy making in the US and Canada. Through two paired comparisons (Florida and California; Ontario and Alberta), we find that policy innovation occurs when publics are engaged in policy formulation through iterative, ongoing public consultation on policy instruments and settings. Reflexive learning among publics and policy elites generates legitimacy, facilitating major policy change.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".