Structural accommodations of patriarchy: Women and workplace gender segregation in Qatar
Bibliographic record
Abstract
As the institutions of classic patriarchy erode in Qatar, women are entering the labour force in growing numbers. It is argued that women's need to work in societies historically characterized by classic patriarchy causes them to enact strategic accommodations that signal their feminine respectability and conformity to male domination. We find the Qatari context to be characterized by structural rather than individual accommodations of patriarchy. State institutions and several employers have made available gender‐segregated workplaces that facilitate women's employment while maintaining many elements of patriarchy. Using semi‐structured interview data with university‐aged Qatari women, we examine attitudes towards employment, specifically those related to gender mixing in the workplace. Young women's narratives reveal complex schemas regarding the acceptability of gender mixing, which depends on characteristics of the working woman, characteristics of the men with whom she must interact in the workplace and the spatial organization of the workplace itself. Women's protection of their reputations, critical to maintaining their families’ support and their own marriageability, emerged as a key motivation for limiting interactions with men. The preference for gender‐segregated workplaces reveals Qatari women's continued subscription to the patriarchal bargain — they constrain their behaviour in return for protection from male kin.
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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 | 0.002 | 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 | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".