Troubling Intersections: Physical Intimate Partner Violence Against Women in Bangladesh
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».