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Record W4312063171 · doi:10.29173/rssj10

Muslim Organizations in Canada

2022· article· en· W4312063171 on OpenAlexaboutno aff
Fatima Chakroun

Bibliographic record

VenueReligious and Socio-Political Studies Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsIslamRealmPublic relationsReligious organizationPoliticsPopulationSociologyScope (computer science)Religious valuesCivil societyPublic spherePhenomenonPolitical scienceGender studiesSocial scienceLawGeography

Abstract

fetched live from OpenAlex

As Canada’s Muslim population has grown since the late 19th century, Muslim organizations have been established and developed to respond to the needs of an increasingly diverse population. Muslim organizations are active in numerous spheres of Canadian society, including but not limited to social services, education, religious practice, politics, and mental and physical wellbeing. While existing literature tends to examine Muslim organizations by type of organization, sphere of operations, or a particular phenomenon, this study presents a composite image of Muslim organizations in Canada as a whole, identifying patterns in how Muslim organizations are established and develop over time, in terms of the scope and focus of their activities. The multi-methods study draws on organizational documents and communications, a survey, and qualitative interviews across Canada. A central finding of the study is that Muslim organizations emerge in response to unmet, specific needs within Muslim communities and that these needs are not limited to the realm of religious practice. Muslim organizations are increasingly engaged in what secular society considers “non-religious” areas of life, reflecting a holistic understanding of religious life and Islam as a comprehensive way of life that does not compartmentalise a secular public life from a private religious one.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score1.000

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 studies0.0030.000
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 head0.018
GPT teacher head0.296
Teacher spread0.277 · 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.

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
Published2022
Admission routes1
Has abstractyes

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