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Negotiating Pluralism in Québec: Identity, Religion, and Secularism in the Debate over “Reasonable Accommodation”

2012· book-chapter· en· W2484418308 on OpenAlexaboutno aff
Geneviève Zubrzycki

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicReligion and Society Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsSecularismMulticulturalismReligious pluralismReasonable accommodationPluralism (philosophy)PoliticsSecular stateReligious identityGender studiesNegotiationSociologyClosetPolitical scienceReligious studiesIdentity politicsLawHistoryPhilosophyEpistemology

Abstract

fetched live from OpenAlex

Abstract Once nicknamed “the priest-ridden province,” Québec is now a strikingly secular place. During the so-called Quiet Revolution of the 1960s, the Québécois dramatically rid themselves of Catholicism, amputating what a new generation of social activists and political figures came to see as a gangrenous limb preventing the healthy development of the nation. Yet religion, it turns out, is present not only in the lives of “others” but also as a skeleton in Québec's closet that is often experienced as phantom limb pain. This became apparent in the debates over the religious practices of cultural minorities, which were at the center of public life from 2006 to 2008. Analysis of the debates reveals that what was at stake was as much about Québec's religious past as it was about its present religious landscape and the challenges it poses for a self-avowed secular society. The Québécois case is also helpful to think about the meaning and stakes of religious pluralism and secularism in contexts very different from that of the United States or France, which offer the prevailing models. Through an analysis of the debates over reasonable accommodation, this chapter shows that Québec is currently trying to find its own way between the French, American, and Canadian models of laïcité, pluralism, and multiculturalism.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.904
Threshold uncertainty score0.666

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 integrity0.0000.001
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 head0.022
GPT teacher head0.312
Teacher spread0.291 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations6
Published2012
Admission routes1
Has abstractyes

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