Early childhood education and care reform in Canadian provinces: Understanding the role of experts and evidence in policy change
Bibliographic record
Abstract
Abstract In over half of Canada's provinces, a recent commission has recommended major reform of ECEC systems; in response, provincial governments in nearly all cases have chosen to implement full‐day kindergarten funded by the province and delivered through public schools. We analyze the commission processes that led to changes to investigate the relationship between experts, policy makers, and policy outcomes. Our analysis of processes in BC, Alberta, Ontario, Quebec, and PEI leads us to conclude that governments rarely fully implement the programs supported by the evidence base and recommended by their own experts. Instead, recent policy changes have been driven by a highly selective and path‐dependent approach to the complex issue of early childhood care and education.
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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 | 0.069 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".