Back to the Basics: Socio-Economic, Gender, and Regional Disparities in Canada's Educational System
Bibliographic record
Abstract
This study reassessed the extent to which socio-economic background, gender, and region endure as sources of educational inequality in Canada. The analysis utilized the 28,000 student Canadian sample from the data set of the OECD’s 2003 Programme for International Student Assessment (PISA) . Results, consistent with previous findings, highlight the uneven distribution of educational achievement in Canada along socio- economic, gender, and regional lines, and point to the continued necessity of policy to mitigate the impact of gender, class, and regional inequalities on the educational out- comes and life chances of young Canadians. Key words: social inequality, educational outcomes, educational aspirations, SES, cultural capital, PISA Dans cet article, les auteurs se demandent dans quelle mesure le statut socioeconomi- que, le sexe et la region demeurent des sources d’inegalite en matiere d’education au Canada. L’analyse repose sur l’echantillon des 28 000 eleves canadiens tire de l’ensemble de donnees du Programme international pour le suivi des acquis des eleves (PISA) de 2003 de l’OCDE. Les resultats, conformes aux conclusions anterieu- res, mettent en evidence la repartition inegale de la reussite scolaire au Canada selon le statut socioeconomique, le sexe et la region et indiquent la necessite d’attenuer l’impact du sexe, de la classe sociale et des inegalites regionales sur les resultats scolaires et les chances d’epanouissement des jeunes canadiens. Mots cles : inegalite sociale, resultats scolaires, aspirations quant aux etudes, statut socioeconomique, capital culturel, PISA
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".