Bibliographic record
Abstract
Alcohol and Islam is a relatively understudied topic, although alcohol abuse is a significant social problem both in Muslim majority countries and among Muslim minorities. Questions of religious identity as they relate to food and drink prescriptions and proscriptions also make alcohol and Islam a worthwhile topic. This article offers a general overview of alcohol and Islam. It briefly introduces alcohol and Islam in history; examines the main Islamic religious sources (the Quran, the sayings and practices of the Prophet Muhammad, and Islamic law); analyzes World Health Organization statistics on contemporary patterns of abstention and alcohol consumption in Muslim majority countries; reviews the social science literature on alcohol studies in Muslim settings; presents Saudi Arabia and Turkey as opposite extremes of prohibition and permissiveness in Muslim majority countries; offers France as a case study of the effects of migration on abstention and drinking patterns of Muslims in minority settings (about a quarter of all Muslims live as religious minorities); and looks at the rationales that some Muslims give for drinking. In conclusion, the article places the problem of alcohol prohibition in a larger context of how to approach food and drink prescriptions and proscriptions; it also cautions against overestimating the influence of Islam, and suggests an agenda for future studies of alcohol-related beliefs and behaviors among Muslims.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".