The continuum of gender-based violence experienced by migrant and refugee women in Canada: perspectives from key informants
Bibliographic record
Abstract
Little research has been done on conceptualizing gender-based violence (GBV) against immigrant and refugee women as a continuum of violence. The objective of the larger study was to understand gender-based violence in migration and analyze the ways in which discriminations and inequalities interact to increase vulnerability and decrease access to supports and services for some women. Using (a) the concept of continuum of [sexual] violence and (b) intersectionality, we demonstrate the need to both document the range of violence in women’s lives and the tactics of victimization among immigrant and refugee women and show how they are different than the cumulated literature showing victimization tactics against the Canadian-born population. Using semi-structured interviews via phone or video, we asked professionals ( N = 43) who worked with migrant women across Canada about forms of GBV experienced in the immigrant and refugee populations they worked with. Participants reported that non-physical forms of violence are more normalized, but also more commonly experienced than physical forms of violence in Canada. Additionally, intersecting social identities impact both the distinct and amplified forms of GBV immigrant and refugee women experienced. Results contribute theoretically and empirically to the conceptualization of the GBV experiences by immigrant and refugee women in Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".