The psycho-social factors that escalate intimate partner violence (IPV) among South Asian women in North America: An intersectional approach and analysis
Bibliographic record
Abstract
South Asian (SA) women, immigrant, and non-immigrant, living in the United States (US) and Canada make up a unique population for social research, specifically as it relates to intimate partner (IPV) or gender-based violence (GBV). Despite an increase in study of GBV or IPV including in the current pandemic situation, research on IPV for South Asian women in North America remains inadequate. There is a disconnect in the literature about how multiple psycho-social factors including socio-cultural, socio-demographic, and individual factors intersect and intensify IPV victimization of South Asian women in the North American context. Factors, like collectivism, patriarchy, and family honor are essential to this discussion. To better understand this gap and mobilize knowledge, this paper seeks to assess, evaluate, and examine the current state of literature on IPV among South Asian women in North America (Canada and US) in an effort to determine the associated psycho-social factors that exacerbate South Asian women’s abuse experiences. Additionally, Kimberlé Crenshaw’s intersectional approach is used to explain how South Asian women’s overlapping multiple identities escalate the IPV experience. The results of this paper identify and articulate several psycho-social factors including individual factors (ethnicity, gender role, young age, education, religion, immigrant identity, English fluency, and economic dependency); social/community factors (acculturation which intersect with limited family support and change of gender roles; patriarchy; family primacy (collectivism and family honor); and limited community support); and the (lack of) service/technology knowledge can affect one’s experiences of violence. Educational and policy recommendations for better intervention and/or prevention of IPV for South Asian women residing in the West is also considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".