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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".