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Record W2910283899 · doi:10.17645/si.v7i1.1613

School Market in Quebec and the Reproduction of Social Inequalities in Higher Education

2019· article· en· W2910283899 on OpenAlexaffabout
Pierre Canisius Kamanzi

Bibliographic record

VenueSocial Inclusion · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsInstitutionInequalityReproductionSocial reproductionCohortSocial stratificationHigher educationPublic institutionSocial inequalitySociologySocial mobilitySocial classDemographic economicsMediationSample (material)Political scienceSocial capitalSocial scienceEconomic growthEconomicsMedicineBiologyMathematics

Abstract

fetched live from OpenAlex

The purpose of this article is to show that the stratification of the Quebec high school market contributes to the reproduction of social inequalities in higher education. The results obtained from a sample (N = 2,677) of a cohort of students born in 1984 and observed up to the age of 22 show that the influence of social origin operates in large part via mediation of the type of institution attended. Students enrolled in private or public institutions offering enriched programs (in mathematics, science or languages) are significantly more likely to access college and university education than their peers who attended a public institution offering only regular programs. Additional analyses reveal that the probability of attending a private or public institution offering enriched programs is strongly correlated with the social origin of the student. The influence of the education market itself operates through differences in performance and educational aspirations that characterize students in three types of establishments.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.019
GPT teacher head0.321
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations29
Published2019
Admission routes2
Has abstractyes

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