Bibliographic record
Abstract
Abstract The long history of Islam in the United States is not well understood. The first Muslims to come to this country were African slaves followed by Muslims from the Ottoman Empire. As time went by, other Muslims from different parts of the world followed suit. Today, Muslims form part of the sociocultural and religious diversity of US society. A unique feature of this community is its diversity, a function of different schools of thought as well as different migration trajectories in terms of ethnicity, gender, age, class, and countries of origin. Its diversity has generated a rich body of knowledge on health care that can enrich the American biomedical model. Yet, this knowledge has been subjugated and remains unrecognized owing to structural exclusion of Muslims exacerbated by 9/11. The aim of this article is to highlight health beliefs and practices of American Muslims with the view to recognizing their contribution to American society, leading to greater acceptance of this community. In sum, beyond addressing systemic exclusion, it is important to recognize that American Muslims have a long history and richness in understanding health in diverse sociocultural milieus in Islam that can and should be recognized in clinical care.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".