Tracing the sub-national effect of the OECD PISA: Integration into Canada’s decentralized education system
Bibliographic record
Abstract
Although education scholars have examined the globalization effect of the Organisation for Economic Co-operation and Development (OECD) Programme for International Student Assessment (PISA) and its impact in several countries, few have explored its effect at the sub-national level. Taking the Canadian federation as its case study, I argue that the PISA, as a universalizing project for education, is being uncritically replicated through the implementation of student assessments at the national level. By drawing on the policy studies and policy sociology literature, I find evidence of policy discursive practices and techniques, which led to the creation and replication of a PISA-modeled assessment sub-nationally in the form of the Pan-Canadian Assessment Program. Three key themes emerge that facilitate the modeling of universalizing educational projects such as the PISA by sub-national entities: (1) a preoccupation with the international benchmarking of student performance, (2) a shift away from a curriculum-based assessment to a competency-based one, and (3) the adoption of organizational systems and processes of assessment aligned with supranational assessment practices. I suggest that domestic conditions in the Canadian federation were conducive to the rapid integration of the PISA sub-nationally despite the decentralized structure of the Canadian elementary and secondary education system.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".