Is Canada really an education superpower? The impact of non-participation on results from PISA 2015
Bibliographic record
Abstract
Abstract The purpose of large-scale international assessments is to compare educational achievement across countries. For such cross-national comparisons to be meaningful, the participating students must be representative of the target population. In this paper, we consider whether this is the case for Canada, a country widely recognised as high performing in the Programme for International Student Assessment (PISA). Our analysis illustrates how the PISA 2015 sample for Canada only covers around half of the 15-year-old population, compared to over 90% in countries like Finland, Estonia, Japan and South Korea. We discuss how this emerges from differences in how children with special educational needs are defined and rules for their inclusion in the study, variation in school participation rates and the comparatively high rates of pupils’ absence in Canada during the PISA study. The paper concludes by investigating how Canada’s PISA 2015 rank would change under different assumptions about how the non-participating students would have performed were they to have taken the PISA test.
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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 | 0.068 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".