It’s Complicated: Canadian Education Policymaking in the Pandemic Era
Bibliographic record
Abstract
Education policymaking is crucial in preparing children to meet future societal challenges. However, policymaking is never straightforward. And the educational policymaking landscape in post-pandemic Canada includes complexities that make evidence-based decision-making particularly difficult. These factors include: the baseline systemic oppression embedded in Canada’s education systems; the tension between the importance of public opinion and the public’s imagined expertise; the inter-disciplinary nature of the field of education research; the challenge of quantifying education outcomes and the simple messages numbers can carry; and the long time-horizons in education payoffs compared to myopic tendencies in politics. Added to these factors is the rise of conspiracy theories and anti-truth sentiment, which undermines trust in expertise, and this sentiment often carries logics of white supremacy and colonialism. This paper identifies these factors in the hopes that policymakers and researchers do not underestimate the difficulty or importance of bringing about evidence-based policy in education settings.
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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 | 0.028 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.045 | 0.035 |
| Scholarly communication | 0.029 | 0.011 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 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".