Trends in Cognitive Skill Inequalities by \nSocio-Economic Status across Canada
Bibliographic record
Abstract
Les auteurs documentent les tendances dans les déficits de capacités cognitives pouvant être observées dans l’ensemble du Canada. Ils utilisent pour cela les résultats aux tests du Programme international pour le suivi des acquis des élèves (PISA) sur sept cycles, de 2000 à 2018, afin de brosser le portrait exhaustif des tendances dans la distribution des résultats aux tests au fil du temps et dans les écarts de résultats selon le statut socio-économique (SSE) parental. Ils constatent que les écarts de rendement entre les meilleurs élèves (90e percentile) et les élèves aux prises avec des difficultés (10e percentile) sont importants et représentent plus de quatre années de scolarité. Les auteurs montrent également que les écarts socio-économiques dans les résultats aux tests du PISA en lecture, en mathématiques et en sciences sont importants mais généralement stables dans le temps. Des variations sont relevées dans les écarts qu’affichent les résultats selon le SSE par province, un indicateur de l’étendue de l’inégalité des chances, mais ces variations sont modestes. Abstract: In this article, we document the trends in cognitive skills gaps across Canada. We use Programme for International Student Assessment (PISA) test scores over seven cycles, from 2000 to 2018, to provide an exhaustive portrait of the trends in the test score distribution over time and the score gaps by parental socio-economic status (SES). We find that the achievement gap between top-performing students (90th percentile) and students facing challenges (10th percentile) is large and represents more than four years of schooling. We also show that socio-economic differences in PISA scores for reading, mathematics, and science are large but generally stable over time. There are variations in SES score gaps by province, a proxy for the extent of inequality of opportunities, but these variations are not large.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".