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Record W4393989167 · doi:10.1177/00084298241238138

Life in abundance: Diet, Black health and spirituality in the Nation of Islam, 1930–1975

2024· article· en· W4393989167 on OpenAlexaffvenue
Nils Harley Duranton

Bibliographic record

VenueStudies in Religion/Sciences Religieuses · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsIslamSpiritualityAbundance (ecology)SociologyReligious studiesPsychologyHistoryDemographyGender studiesMedicineBiologyPhilosophyAlternative medicineEcology

Abstract

fetched live from OpenAlex

This article aims to analyse the relationship between the Nation of Islam and dietary rules, as well as to show how its doctrinal component is linked to a quest for an African American redemption. Although this Black nationalist organization claims to remain within the fold of the Islamic religion, historical research conducted in the 1990s and 2000s has stressed that its message is characterized by peculiar beliefs and religious practices. The author’s analysis relies on writings that were produced by the Nation of Islam – mainly, the two volumes of How to Eat to Live, authored by Elijah Muhammad. These dietetic teachings appear to bond the movement to various new religious movements, mainly of Christian inspiration. It appears probable that this set of beliefs was influenced by the German naturopath Arnold Ehret (1866–1922), thus leading to a significant difference with Sunni Islam.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.145
Threshold uncertainty score0.288

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.092
GPT teacher head0.425
Teacher spread0.333 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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