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Record W3015431521 · doi:10.1177/0037768619895152

Open secularism and the RCMP turban debate

2020· article· en· W3015431521 on OpenAlexaffabout
Richard D. Mann

Bibliographic record

VenueSocial Compass · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicSouth Asian Studies and Diaspora
Canadian institutionsCarleton University
Fundersnot available
KeywordsSecularismOfficerGovernment (linguistics)LawArgument (complex analysis)Political scienceSociologyMedicinePhilosophy

Abstract

fetched live from OpenAlex

This article examines newspaper articles and opinion pieces related to the 1989 and 1990 case of allowing RCMP (Royal Canadian Mounted Police) officers to wear turbans as part of their formal uniform. Many of those opposed to allowing for this change in RCMP policy demonstrate a sense of an assumed national identity that tends to label immigrants and people from non-European backgrounds as un-Canadian. Once the federal government approved this change in RCMP policy, some of the groups that opposed it attempted to bring it to the Supreme Court of Canada. The argument they made was one for closed secularism. The policy change, however, and the impact it had on Baltej Singh Dhillon, the first Sikh RCMP officer who became an officer and was allowed to wear his turban the results of which present a case for open secularism.

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)0.012
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0310.064
Scholarly communication0.0120.006
Open science0.0010.011
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0060.000

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 head0.066
GPT teacher head0.249
Teacher spread0.182 · 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 designNot applicable
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
Published2020
Admission routes2
Has abstractyes

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