Currently married women’s present experiences of male intimate partner physical violence in Bangladesh: An intercategorical intersectional approach
Bibliographic record
Abstract
In Bangladesh, one in five currently married women (CMW) presently experience male intimate partner physical violence (MIPPV). While previous studies analysed women’s individual-level multiple locations–younger age, lower education, income, and poverty in an additive manner, we took an intersectional approach to look at the effects of their multiple intersectional locations on MIPPV. Using McCall’s intercategorical intersectional approach, we examine how women’s intersectional locations are associated with their odds of experiencing MIPPV. Our sample from a 2015 nationally representative survey comprised 14,557 CMW living with their spouses. Thirty-four percent of CMW are young, 49% below primary educated, 19% income earning, 23% poor, and 25% experience MIPPV. We found that CMW in their dual disadvantaged younger age–lower education and single disadvantaged higher education–poor locations have 13.57% (95% CI, 9.25, 17.89) and 12.02% (95% CI, 6.87, 17.17) (respectively) higher probabilities of experiencing MIPPV than their counterparts in the corresponding dual privileged older age–higher education and higher education–nonpoor locations. Consistent with intersectionality theory, instead of prioritising a few groups over others (i.e. Oppression Olympics), we recommend building intersectional solidarity with women, men and communities to disrupt the underlying socio-economic-educational-legal-political structures and processes that have sustained these marginalised locations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".