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Record W3095179496 · doi:10.1177/0008429819858925

Does Place Matter? Burial Decisions of Muslims in Canada

2020· article· en· W3095179496 on OpenAlexafffundvenueabout
Güliz Akkaymak, Chedly Belkhodja

Bibliographic record

VenueStudies in Religion/Sciences Religieuses · 2020
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsConcordia UniversitySeneca Polytechnic
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMulticulturalismIslamImmigrationInterpretation (philosophy)Relation (database)PreferencePopulationSense of placeSociologyGender studiesGeographyArchaeologyDemographySocial science

Abstract

fetched live from OpenAlex

This paper is concerned with the complex relationship between immigration, religion, burial decisions, and a sense of belonging. Drawing upon a case study of Muslims in London, Ontario, Canada, we examine Islamic funeral and burial services available in the city and the preferred burial locations of its Muslim communities. Our interviews with different immigrant generations of Muslims show that participants, regardless of their immigrant generation, prefer London as a location of burial for themselves and their loved ones. We argue that four major factors at the structural and individual level shape the preference of study participants with respect to the location of burial: access to an Islamic cemetery and Islamic funeral services; an established Muslim population in the city; relation to and interpretation of religious requirements; and a sense of belonging to Canada. We discuss the findings in relation to multiculturalism and recognition of cultural and religious differences.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.004
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.078
GPT teacher head0.392
Teacher spread0.314 · 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

Citations5
Published2020
Admission routes4
Has abstractyes

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