How Sexual and Gender-Based Violence Affects the Settlement Experiences Among Yazidi Refugee Women in Canada
Bibliographic record
Abstract
Gender and sexual violence is historically used as a weapon of war. Yazidi women resettled in Canada directly from northern Iraq after the 2014 Daesh-led attacks in the Sinjar region. This direct resettlement experience makes the Yazidi refugees a very distinct group from a resettlement perspective. The severe human rights violations and sexual and gender-based violence they have experienced has affected both their physical and mental health. However, research on pre-arrival trauma and its impact on resettlement has been limited to individual post-arrival psychological interventions without considering how pre-arrival trauma experiences may affect their overall settlement experience. Our paper focuses on the settlement challenges and needs of 21 Yazidi women resettled in the four Canadian cities with the largest Yazidi communities. Because the resettlement of the Yazidi often happened within weeks after their release from captivity, the structural deficiencies within the Canadian settlement network revealed challenges for resettlement organizations in terms of how they assist those with acute trauma. We argue that although the Canadian resettlement program is generous in many ways, it falls short of adequately addressing trauma at the acute stage, especially sexual and gender-based violence as experienced by the Yazidi women and children. Our analysis reveals that single-female-headed families, particularly those with young children, have a difficult time navigating the resettlement system in Canada. We have identified the resettlement experiences of Yazidi women and recommend resettlement to happen in three stages, to account for the acute level of trauma this particular group faces. The first stage lasts between six weeks and three months as many women require more dedicated support from settlement providers for housing, language, and health. The second stage is a period of adjustment which occurs within the next eighteen months, depending on the available support these refugee women have to navigate the different settlement services. The third stage begins sometime after the second year when many women can start navigating the social support, education and health systems independently. Thinking of how SGBV may influence the resettlement process in these three stages is a good way for us to consider the additional assistance that may be needed and how they may better access resettlement services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".