Violence and its influence on the wellbeing and integration of immigrant and refugee women in Canada.
Bibliographic record
Abstract
Violence against women has severe health and social repercussions for everyone involved. As a result, gender-based violence (GENDER-BASED VIOLENCE) has become a greater concern for activists, feminists, scholars, and increasingly, migration workers. Although the issue of gender-based violence is the subject of much research, the experiences of newcomer women are often ignored. Several factors make newcomer women more vulnerable to GENDER-BASED VIOLENCE in their new country. These factors are exacerbated by migration, change in environment, socioeconomic status, cultural differences, language barriers, and lack of awareness of resources that prevent them from receiving assistance. Studies have shown that immigrant and refugee women face additional systemic, interpersonal, sociopolitical and cultural barriers that are reproduced through the interaction of multiple forms of identity dimensions and forms of oppression (Jayasuriya, 2018; Thurston et al., 2013). My study is informed by the intersectional theory which is used to understand the experience of newcomer women survivors. My research is part of a larger international project examining the experience of gender-based violence among immigrant and refugee women in seven countries (Freedman et al., 2021). I conducted six (6) semi-structured interviews with immigrant and refugee women in British Columbia, Alberta, Saskatchewan and Manitoba to answer the question, “How does violence against immigrant and refugee women affects their psychological well-being and integration process in Canada? My results indicate that lack of safe and alternative housing, social support, access to social services, financial independence coupled with unemployment, and lack of awareness are major reasons for newcomer women not seeking or leaving abusive relationships. I conclude by providing some suggestions regarding policy and programing around newcomer women and GENDER-BASED VIOLENCE.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".