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Record W3141189051 · doi:10.1080/15507394.2021.1881028

Religious Literacy in Law: Anti-Muslim Initiatives in Quebec, the United States, and India

2021· article· en· W3141189051 on OpenAlexaboutno aff
Amarnath Amarasingam, Hicham Tiflati, Nathan C. Walker

Bibliographic record

VenueReligion & Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMulticulturalism, Politics, Migration, Gender
Canadian institutionsnot available
Fundersnot available
KeywordsFunctional illiteracyPersecutionLawPolitical scienceHinduismReligious persecutionReligious discriminationLegislatureLiteracySociologyReligious studiesPolitics

Abstract

fetched live from OpenAlex

This inquiry examines how religious illiteracy amongst politicians, legislators, lawyers, and judges could lead to discriminatory legislations and increased persecution against religious minorities. We look at laws and legislations in three contexts, Quebec, the USA, and India, where anti-religious sentiments and violence are predominantly directed against Muslims. More specifically, we examine Quebec’s legal attempts, in the form of exclusive laïcité, to regulate religion and religious signs within the province; the recent ruling about converting the Babri mosque in India into a Hindu Temple; and anti-Shariah laws in the United States. Taken together, these justifications for anti-Muslim bills perpetuated illiteracy about both religion and the legal systems, leading the conditions for discrimination against religious minorities. We conclude that religious illiteracy, more specifically on the level of judiciaries and legislative bodies, presents a pressing threat to the safety and wellbeing of religious minorities, Muslims in particular, around the world.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.390
Teacher spread0.349 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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