La contribution de l'école privée au Québec à la littératie et à la numératie des 15 ans: une analyse par effets de traitement
Bibliographic record
Abstract
(in French) Les élèves québécois au niveau secondaire sont considérés comme relativement performants sur la base de la moyenne générale obtenue aux tests internationaux (math, lecture, sciences) PISA. Cependant, il est peu connu que les scores des élèves du secteur privé, fortement présents à l'école secondaire, augmentent considérablement les moyennes rapportées. Néanmoins, une école avec des scores élevés à des tests standardisés peut être une école où les étudiants viennent de milieux très avantagés ou encore sont sélectionnés sur la base de leurs aptitudes ou classe sociale. L'impact de la non-randomisation sur l'estimation d'un effet de traitement pose des défis analytiques comme les biais de sélection, de causalité ou de recrutement qu'il faut réduire et neutraliser. Sur la base des cinq enquêtes PISA (2000, 2003, 2006, 2009, 20012), quatre types d'estimateur économétrique d'appariement pour évaluer l'effet causal de traitement de l'école privée sont calculés. L'analyse conduit aussi à un exercice de falsification avec tous les élèves ontariens comme groupe non traité relativement aux élèves québécois en secondaire IV du secteur public ou du secteur privé. Les résultats suggèrent que la minorité importante des élèves qui fréquentent l'école privée au secondaire explique en partie la performance supérieure du Québec aux tests PISA, tant dans les écoles privées que publiques (par effet de concurrence). Selon les estimés plus conservateurs, l'école privée ajoute approximativement une année d'études en termes des scores de lecture et math. De façon plus importante, pour le développement du capital humain, les résultats soutiennent l'existence d'un effet causal important de l'école privée sur les niveaux (hiérarchie) mesurés des compétences par PISA. <p> Abstract (in English, working paper is in French) Québec's students at the secondary level have always been considered as a relatively performing group on the basis of their mean scores in international skills tests (reading, math, sciences) such as PISA, and TIMSS. It is less known that students' performance in private schools, where a significant proportion are enrolled at the secondary level, raise substantially the reported mean scores. Nonetheless, a school with high standardized scores may be an institution where students come from a much advantaged background or are selected on the basis of their abilities or social class. The impact of non-randomness for the estimation of a treatment effect poses analytical challenges like bias related to selection, causality, or recruitment that must be reduced or neutralized. There are many categories of estimators that can be implemented to assess a non-random policy/intervention based on models for both the outcome variable and the treatment assignment, while taking into consideration the selection bias. We use rich student-level micro data from five (2000, 2003, 2006, 2009, 2012) Programme for International Student Assessment (PISA) and four types of estimators to estimate the impact of private schools on student performance in reading, math, and sciences. The analysis also conducts a falsification exercise with students from Ontario as a control group relative to Québec's students in the private or public sector. Results suggest that the large minority of students attending private schools at the secondary level largely explains the higher PISA scores of Québec over the years, as well as the scores in the public sector (due to external effects from school competition). According to the more conservative treatment effects, private schools add approximately one year of study in reading and math scores. Results highlight the major importance of increased 'knowledge capital' by the private sector, that is proficiency scales in cognitive skills. The characteristics of schools and teachers in the private sector, and the socio-economic status of students and their parents, are also important factors of achievement gaps between the two systems of education.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".