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Shi‘i Islam in the African American Community

2014· book-chapter· en· W2208736161 on OpenAlexaff
Liyakat Takim

Bibliographic record

VenueOxford University Press eBooks · 2014
Typebook-chapter
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCuriosityIslamScholarshipMuslim communityEthnic groupAfrican americanPolitical scienceGender studiesSociologyReligious studiesHistoryLawAnthropologyPsychologySocial psychologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract While much has been written regarding the rise and experience of the African American Muslim community, western scholarship has paid little attention to the African American Shi‘is. This chapter argues that, because it is a minority within the Muslim community in America, the Shi‘i community is highly introverted and more concerned with preserving rather than extending its boundaries. In addition, the ethnic divisions within the Shi‘i community and the fact that Shi‘ism is highly reliant on the foreign-based leadership means that the Shi‘i community has not been concerned with reaching out to potential converts. This chapter also argues that by their vehement attacks on the Shi‘is, the Wahhabis have aroused the curiosity of many African American converts who may have not heard of Shi‘ism. Paradoxically, this has led to their conversion to Shi‘ism. Finally, the chapter highlights instances of African American Shi‘i–Sunni altercations in correctional facilities.

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)0.000
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 score0.010
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.006
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
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 head0.030
GPT teacher head0.239
Teacher spread0.210 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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