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Record W2512083037 · doi:10.47678/cjhe.v46i2.184865

Immigration et cheminements scolaires aux études supérieures au Canada : qui y va et quand ? Une analyse longitudinale à partir du modèle de Cox

2016· article· fr· W2512083037 on OpenAlexafffundvenueabout
Pierre Canisius Kamanzi, Nicolas Bastien, Pierre Doray, Marie‐Odile Magnan

Bibliographic record

VenueCanadian Journal of Higher Education · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversité du Québec à MontréalUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Le présent article vise à analyser les cheminements scolaires de jeunes Canadiens issus de l’immigration qui font des études supérieures. Pour ce faire, nous utilisons le modèle de risque proportionnel de Cox. Les résultats obtenus à partir des données longitudinales de l’Enquête auprès des jeunes en transition (EJET) montrent que le risque d’accès aux études supérieures est plus élevé chez les étudiants issus de l’immigration que chez leurs pairs dont les parents sont Canadiens de naissance. La différence varie de 3 à 35 points de pourcentage parmi ceux de première génération et de 4 à 13 points de pourcentage parmi ceux de deuxième génération. Par ailleurs, il existe des différences marquées entre les différents groupes d’étudiants issus de l’immigration, en ce qui concerne l’âge d’entrée aux études supérieures, la persévérance et le type de diplôme obtenu à l’âge de 24 ans. Bien que cette différence diminue relativement lorsqu’on tient compte des ressources des parents et des variables associées à l’expérience scolaire de l’élève au secondaire, elle demeure statistiquement significative.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.309
Teacher spread0.276 · 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; a candidate call from one teacher head, not a consensus.

Study designNot applicable
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

Citations17
Published2016
Admission routes4
Has abstractyes

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