Bibliographic record
Abstract
While much has been written regarding the rise and experience of the African-American Muslim community in America, Western scholarship has paid little attention to the Black Shi‘is in the country. This paper will attempt to redress this imbalance. First, the paper will discuss Shi‘i institutions and their proselytization activities in America. It will be argued that, being 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 ayatollahs means that the Shi‘i community has not been concerned with reaching out to potential converts. Drawing upon the results of a survey conducted for this study, it will be argued that the Wahhabis, by their vehement attacks on the Shi‘is, have aroused the curiosity of many converts who had not previously heard of Shi‘ism. Paradoxically, this has led to their conversion to Shi‘ism. The paper will also review the situation of Shi‘ism in the Correctional Facilities and analyze the works of a major Shi‘i proselyte whose enormous impact on inmates of American correctional facilities has yet to be acknowledged.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".