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Record W2922909427

The Shift from Religious to Linguistic Schooling in Quebec: An Analysis of the Public Discourse and the Political Obstacles, 1960-1997

2018· article· en· W2922909427 on OpenAlexaffabout
Anthony Di Mascio

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Political Studies
Canadian institutionsBishop's University
Fundersnot available
KeywordsSecularizationConstitutionReligious educationPoliticsProtestantismPolitical scienceSecular educationPublic opinionSociologyLegislatureEstablishment ClausePublic discourseLawPublic administrationFirst amendment
DOInot available

Abstract

fetched live from OpenAlex

Through an analysis of print media and the legislative debates and reports on education and the separate school question in Quebec, this paper aims to better understand the shift from religious to linguistic schooling in post-Quiet Revolution Quebec. Once the strongest defender of religious school rights in Canada, the secularization of Quebec over this period saw it abandon its denominationally-based school system. Public opinion on education is juxtaposed in this paper with the political discourse surrounding the movement toward secular schooling in Quebec. This paper finds that while there was considerable appetite for the abolishment of Quebec’s denominational school system, the legal protection afforded to Catholics and Protestants in section 93 of the Canadian Constitution proved to be a major hurdle in the political arena. After several decades of effort, Quebec eventually received a constitutional amendment in 1997 that allowed it to replace its Catholic and Protestant schools with a linguistically-based French and English school system.

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)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.953

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0160.006
Scholarly communication0.0060.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.032
GPT teacher head0.332
Teacher spread0.300 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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Same venue2019 Conference of the Canadian Society for the Study of EducationSame topicHistorical and Political StudiesFrench-language works237,207