Bibliographic record
Abstract
Abstract Overall, early years policy in Canada has a mixed record, with some evidence of policy success and some evidence of policy failure. Nonetheless, bright spots within the early years landscape warrant exploration and deserve to be cast as successes in policy development that can either be replicated across other provincial jurisdictions or explored further by national governments. They include the federal Canada Child Benefit (CCB) program, Ontario’s full-day kindergarten (FDK) model, and Québec’s maternity and parental leave program. Complicating the successful implementation of early years policy is the federal nature of Canada. Federal investment in early years programs has waxed and waned over the decades, and has relied primarily on the use of tax instruments and the federal spending power. The bulk of policy action lies with provincial and territorial governments which hold substantive jurisdiction, and which have relied on a mix of policy approaches. As such, this chapter examines early years policy innovations across both orders of government.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".