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Record W4200549399 · doi:10.1080/03906701.2021.2015933

School market and the democratization of education: one step forward, two steps back. The case of the Canadian Province of Quebec

2021· article· en· W4200549399 on OpenAlexaffabout
Pierre Canisius Kamanzi

Bibliographic record

VenueInternational Review of Sociology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsUniversité de MontréalCégep Marie-Victorin
Fundersnot available
KeywordsDemocratizationAttendanceSocioeconomic statusSocial classEthnic groupInequalityEducational inequalitySocial inequalitySociologyHigher educationReproductionDemographic economicsPolitical scienceSociology of EducationMediationInstitutionCohortDemographySocial scienceEconomicsDemocracyPopulationPoliticsMedicineLaw

Abstract

fetched live from OpenAlex

The purpose of this article is to show that since the 1980s, educational inequalities in Quebec have gradually been reconfigured through the school market. The results obtained from a sample (N = 24,085) of a cohort of students who entered their first year of secondary school in 2002–2003 and who were observed for up to ten years (2012–2013) show that the influence of social origin on educational inequalities operates in large part via mediation of the type of institution attended. The results indicate that enrollment in selective schools, whether public or private, is closely associated to the social characteristics of the students: socioeconomic and ethnocultural origin, gender and mother tongue. In addition, there is a strong association between attendance at these same institutions, access to higher education and graduation. We conclude that the persistence of the reproduction of social inequalities in education is the result of the interactive and combined effects of social power relations related to class and ethnicity, and the current organization of public policies in education. This reveals a paradox as school markets have been promoted by public policies in the name of strengthening democratization and improving the quality of education.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.331
Teacher spread0.315 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations7
Published2021
Admission routes2
Has abstractyes

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