Precarious employment and gender-based violence against migrant women: A scoping review mapping the intersections
Bibliographic record
Abstract
The risk of gender-based violence (GBV) against migrant women is largely exacerbated by precarious employment opportunities available to them as they go through the resettlement process. Despite the risk that the connection of precarious employment and GBV pose to migrant women's health and wellbeing, critical gaps exist in literature. Our scoping review sought to identify and synthesize evidence on the interconnectedness of GBV and precarious employment among migrant women. Six electronic databases were searched for empirical literature and two reviewers independently conducted title/abstract and full text screening of studies that met the inclusion criteria. Data synthesis was guided by the intersectionality theory and the Feminist Political Economy framework. 50 articles met the criteria for inclusion in this review. Our findings reveal that precarious employment plays both a catalytic and consequential role in GBV. Findings highlighted how post-migration shifts in gender roles, schedule unpredictability leading to work-life imbalance, and debt bondage trap migrant women in cycles of exploitation and abuse. Few studies highlighted how human trafficking is intertwined with precarious labor markets, where the exploitation and abuse of migrant women mirror the characteristics of human trafficking. This review underscores the urgent need for integrated policy responses that are not only focused on individual supports but also address the structural drivers or labor precarity and protect migrant women from GBV and human trafficking. By applying an intersectional lens, policies and intervention programs can tackle systemic oppression across economic, and social systems essential in reducing exploitation and abuse to advance migrant women's wellbeing.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".