Les inégalités provinciales aux tests internationaux-nationaux de littéracie : Québec, Ontario et autres provinces canadiennes 1993-2018 (Version révisée et augmentée octobre 2020)
Bibliographic record
Abstract
This paper presents standardised test scores results in literacy from 19 provincial and international surveys in education conducted over years 1993 to 2018. The analysis draws on students in Canadian provinces, mainly in Québec and Ontario, at three stages of education, grade 4 in primary school, grade 8 in secondary school, and -13, -15 and -16-year-olds in grades 8, 9, 10, 11 and 12. The tree domains of literacy are reading, math, and science. A diversity of summary statistics are computed (number of respondents, mean, standard deviation, percentile scores), as well as gaps (P90-P10 and P75-P25) between scores and differences between Québec and other participant group entities. The investigation also displays the distribution of proficiency scale scores. The socio-economic gradients in scores as measured by parental educational and occupational categories are also displayed and discussed in the paper. The results contradict the conclusion of an independent Council in Education, and imply that Québec’s students in particular the less skilled perform as well or better than students in the other provinces system, most of the time with less inequality in literacy domains. The assessment reviews briefly why students in independent schools are more successful and identifies some policy options in education policy addressing social inequities in the skills and knowledge of students. Cet article présente les résultats à des tests standardisés en littéracie, de 19 enquêtes internationales ou provinciales conduites en éducation de 1993 à 2018 auprès d’étudiants du Québec et de provinces canadiennes. L’analyse s’appuie sur les scores à trois stages d’études, 4e année au primaire, 8e année au secondaire et aux étudiants de 13, 15 ou 16 ans (en secondaire II à V). Les domaines de littéracie sont lecture, math et science. Plusieurs types de statistiques sont calculées (nombre de répondants, moyenne, écart-type, scores à divers points de la distribution centile des scores) ainsi que les écarts entre les scores (C90-C10 et C75-25), ainsi que des différences centiles entre le Québec et les entités participantes. L’analyse présente aussi la distribution des étudiants dans les échelles de compétences. Les liens entre scores et les caractéristiques du statut social des élèves, mesurées par l’éducation et les professions parentales, sont documentés pour chaque enquête. Les résultats sont comparés avec ceux partiels du Conseil supérieur de l’éducation. Il apparaît que les élèves québécois, le plus souvent, performent mieux ou aussi bien que ceux des autres provinces aux plans des scores, des écarts centiles et des différences selon le statut social. La discussion finale porte sur quelques options de politique publique en éducation susceptibles de réduire les écarts de littéracie selon le statut social des élèves.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".