The Intersectional Oppressions of South Asian Immigrant Women and Vulnerability in Relation to Domestic Violence: A Case Study
Bibliographic record
Abstract
South Asians ― persons who can trace their origins to India, Pakistan, Sri Lanka, Nepal and Bangladesh ― are the largest racialized minority group in Canada. The National Household Survey (2011) revealed that 1,567,400 persons reported being of South Asian origin, making up 4% of the total Canadian population (Statistics Canada, 2013). The substantial presence and rapid growth of this minority group make it an important population to understand in terms of their settlement and integration-related experiences.\nThe authors of this paper bring together their unique disciplinary lenses- social work and law - to discuss various factors that contribute to the multiple oppressions experienced by South Asian immigrant women in Canada. The paper also focuses on the particular vulnerability newcomer immigrant women can face in situations of domestic violence.\nThis paper is divided into four sections. The first section reviews the literature on the multiple oppressions experienced by newcomer South Asian women and their vulnerability in relation to domestic violence. In the second section the authors present the case of Tejinder, an immigrant woman whom the first author interviewed during data collection for her doctoral dissertation. In the third section the authors discuss how language, gender, race, class and immigration policy intersect to increase the vulnerability of Tejinder in relation to domestic violence. The paper concludes with recommendations for social work practice and for policy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".