Troubling Intersections: Physical Intimate Partner Violence Against Women in Bangladesh
Bibliographic record
Abstract
In the underbelly of beautiful Bangladesh lies the widespread practice of male intimate partner physical violence (MIPPV) against women. Although women’s different socio-demographic risk factors for MIPPV are known, whether their intersecting individual-, community-, and cross-level social locations shape their MIPPV experiences across Bangladeshi communities have not been examined. Therefore, applying Crenshaw’s intersectionality theory, the overarching objective of this dissertation was to make visible the currently married women’s different intersectional social locations that shape their experiences of MIPPV in Bangladesh. McCall’s intercategorical intersectionality approach guided this research. Study participants comprised 14,557 (Studies 1 and 2) and 15,421 currently married women (Study 3) across 911 communities from the 2015 Bangladesh Violence Against Women Survey dataset. Two-level logistic regression models were used to predict women’s MIPPV experiences in the past year and estimate the predicted probabilities at women’s each intersectional location. These probabilities were compared to generate different configurations of inequalities. Study 1 findings indicated that younger age, lower educated and higher educated, poor women compared to older, higher educated and higher educated, nonpoor women had 13.57% (95% CI, 9.25, 17.89) and 12.02% (95% CI, 6.87, 17.17) higher probabilities of experiencing MIPPV. Study 2 found that women living in higher-earning-participation, higher-educated communities had higher probabilities of experiencing MIPPV than those in lower-earning-participation, higher-educated communities (29.90%, 95% CI 25.66 to 34.15 vs. 23.85%, 95% CI 22.40 to 25.30). While our specific hypotheses regarding differences between disadvantaged and advantaged communities were not supported, Study 3 found significant within community differences: younger, poor compared to older, nonpoor women had significantly higher MIPPV probabilities (the minimum difference=12.7%, 95% CI, 2.8, 22.6) in all communities. Similar trend was observed between younger, lower educated compared to older, higher educated women in all except poor communities. Thus, using intersectionality theory made visible Bangladeshi women’s troubling intersections of experiencing MIPPV. Future research might examine the structures and processes that put women at these precarious locations to ameliorate their socio-economic-educational inequalities and reduce MIPPV in all communities. This intersectionality theory-oriented research might advance scholarship on MIPPV in Bangladesh and quantitative intersectionality globally.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".