“Waha ehsa tha, idhar ehsa hai” (It was like that back home, but it is like this here): Family violence experiences of Indian and Pakistani immigrant women in the Greater Toronto Area
Bibliographic record
Abstract
There is a paucity of qualitative scholarship on Indian and Pakistani immigrant women’s experiences of family violence. Further, existing scholarship on this topic seldom explores the unique experiences of distinct South Asian groups such as Indian and Pakistani immigrant women. This thesis addressed this gap in the literature by qualitatively examining family violence among immigrant Indian and Pakistani women in the Greater Toronto Area (GTA). A case study methodology was used to explore two research questions: 1) What are the cultural specificities of family violence as experienced by Indian and Pakistani immigrant women in the GTA? and 2) How are their experiences situated within an immigration context? Data was collected through semi-structured interviews with three women with lived experiences of family violence and six service providers who serve this population. By drawing on multiple theoretical frameworks of feminism, postcolonialism, resilience and immigration, a thematic analysis of the narratives revealed three major themes: 1) Specificities of violence through a cultural lens, 2) Barriers to service and 3) Resiliency: From victimhood to survivorship. Finally, significant implications and recommendations are offered to incorporate these findings within the practice, research and educational arenas of social work.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".