The Weight of Intersecting Identities: A Mixed-Methods Systematic Review of Literature on Community and Systemic-Level Gender-Based Violence Against Visible Minority Women
Bibliographic record
Abstract
Gender-based violence (GBV) affects one in three women globally, with visible minority women being at a higher risk due to the intersection of sexism, racism, and xenophobia. Community-level GBV is a form of interpersonal violence that occurs in public spaces and is enacted by strangers, while systemic-level GBV is linked to institutional and public policies and reflected in inequalities in employment, access to resources, and exploitation. This review examined community and systemic-level GBV against visible minority women, applying an intersectional lens. We conducted a mixed-methods systematic review of academic and gray literature from the last 20 years, following Joanna Briggs Institute’s guidelines. The review focused on community and systemic-level GBV against visible minority women in Canada, the United States, the United Kingdom, Australia, and New Zealand. Our systematic search found 8,970 potentially relevant studies after duplicate removal. Following a two-stage screening process, we identified 35 articles that reported on community and systemic-level GBV. We extracted and integrated data from eligible quantitative and qualitative studies, then thematically synthesized the findings using Thomas and Harden’s approach. Findings demonstrate that visible minority women’s GBV experiences are multifaceted and compounded by intersectional identities. Visible minority women with precarious migration and employment status face particular vulnerability. Barriers based on language, culture, and financial background also add to the difficulties. The intersection of gender, race, and other identities produces severe physical and psychological consequences and the propagation of race and gender-based discriminatory practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".