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Record W2550903168

Les résultats éducatifs de long terme des élèves de l'école secondaire privée au Québec : une évaluation des effets de traitement avec données longitudinales

2016· preprint· fr· W2550903168 on OpenAlexaboutno aff
David Lapierre, Pierre Lefèbvre, Philip Merrigan

Bibliographic record

VenueEconstor (Econstor) · 2016
Typepreprint
Languagefr
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt
DOInot available

Abstract

fetched live from OpenAlex

(in French) Peu d'études peuvent évaluer les effets de long terme de l'école privée secondaire sur les résultats éducatifs des élèves, tout en contrôlant pour les caractéristiques des élèves et des parents. Au Québec, la deuxième province canadienne la plus populeuse, plus de vingt pourcent des élèves du secondaire fréquentent les écoles privées subventionnées par l'État, mais avec frais de scolarité plafonnés. Les biais de sélection, de causalité et de recrutement causés par la possibilité pour l'école privée de sélectionner les élèves rendent toutefois inappropriée une comparaison simpliste de leurs résultats éducatifs par rapport à leurs pairs du secteur public. Cette étude utilise les quatre premiers cycles de deux cohortes longitudinales de l'Enquête sur les jeunes en transition (EJET) réalisée par Statistique Canada. Elle estime l'effet de traitement de l'école privée sur le taux de graduation du secondaire selon le temps attendu, la fréquentation d'un CEGEP à 19 ans, la fréquentation de l'université à 21 ans ou plus, la graduation universitaire à partir de 24 ans ou plus ainsi que l'inscription aux programmes menant à des professions régies par des ordres professionnels au Québec. L'analyse économétrique estime les effets de traitement selon l'appariement par balancement entropique prenant en considération plusieurs variables clés dont le statut socioéconomique des élèves. Les résultats sont ensuite validés par une simulation de variable confondante. Les effets significatifs et robustes estimés attribuables à l'école privée expliquent plus de 56 de l'écart observé entre les élèves par les données (administratives ou descriptives de l'EJET), et près de 81 pourcent selon le modèle, la cohorte et le sexe. <p> Abstract (in English, working paper is in French. See GRCH WP16-02 for English.) Very few studies analyze the long term educational effects of private secondary school students while controlling for their socioeconomic status. In Quebec, the second most populous Canadian province, twenty percent of students at this level are enrolled in private schools subsidized by the government, who however set a relatively low ceiling for the fees in exchange for subsidies. Bias from selection, causality and admission coming from the fact that private schools may select their students, give way to inappropriate simplistic comparison of their educational results with their public sector peers. This study uses the first four longitudinal waves on the two cohorts of Statistics Canada's Youth in Transition Survey (YITS). The analysis estimates the average treatment on the treated the effect of private school on secondary school graduation rate within expected number of years (5), enrollment in postsecondary institutions at age 19, university enrollment at age 21 or more, university graduation at age 24 or more, and enrollment in professional degrees program. The econometric analysis of treatment effects is based on a particular entropy balancing algorithm with a large set of key balancing covariates. Results are validated by a simulation-based sensitivity analysis for matching estimators. We find large, positive and statistically significant effects of private schooling on almost all outcomes analyzed. The results are not sensitive to simulations of omitted variable bias.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0030.007
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.037
GPT teacher head0.289
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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