Employment discrimination faced by Muslim women wearing the hijab: exploratory meta-analysis
Bibliographic record
Abstract
This study tested the hypothesis that Muslim women who wear the hijab are disadvantaged in employment processes relative to their counterparts who do not wear the hijab. A meta-analysis synthesized the findings of seven studies published between 2010 and 2020. The sample-weighted, pooled estimate among the most internally valid, experimental studies suggested that the chances of being hired and so gainfully employed were 40% lower among Muslim women wearing the hijab than they were among, otherwise similar, Muslim women not wearing the hijab: relative risk (RR) = 0.60 within a 95% confidence interval (CI) of 0.54, 0.67. This religion-based discrimination effect was deemed hugely significant in human, public health and policy senses. Immigration trends suggest that millions of Muslim women in the west likely experienced such employment discrimination over the past generation, and millions more are bound to similarly suffer over the next generation if policy status quos are retained. It seems that much of the relatively greater employment discrimination experienced by Muslim women who wear the hijab is due largely to potential employers' prejudicial reactions to the hijab itself. Practice and policy implications and future research needs are discussed.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.030 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".