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Record W4392945939 · doi:10.32920/23589678.v2

Exploring the racial and gendered dimensions of Québecois secularism

2024· preprint· en· W4392945939 on OpenAlexaboutno aff
Nooreen Hussain

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicReligion and Society Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsSecularismGender studiesSociologyPolitical sciencePoliticsLaw

Abstract

fetched live from OpenAlex

This paper explores the specificity of Québec's secularism policies, practices and approach from a Critical Race Theory lens and problematizes apparent contradictions. The recent passing of Bill 21 in Québec necessitates a re-examination of state secularism laws given the discriminatory impact on religious minorities, particularly veiled Muslim women. Specifically, this paper challenges the idea that these laws work to safeguard women's rights and argues that it indirectly accomplishes the opposite. An analysis of the political context highlights the centrality of secularism to conceptions of Québec'ss national identity, while the historical context reveals a complex relationship between Catholicism, religion, identity and secularism. Secularism measures from the past 15 years have carried significant racial and gendered undertones and has relied on a racializing discourse of the Islamic veil. This discourse is contextualized within the specific context of post-9/11 securitization where Islamophobia and anti-Muslim racism have become normalized.

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)0.002
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 score0.042
Threshold uncertainty score0.232

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.006
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.129
GPT teacher head0.355
Teacher spread0.226 · 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
Published2024
Admission routes1
Has abstractyes

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