Women's Participation in Foreign Labour Migration and Spousal Violence: A Study on Returnee Women Migrant Workers in Nepal
Bibliographic record
Abstract
While existing studies point to a high degree of physical and sexual violence 1against women in Nepal, there is a lack of studies assessing the extent of violence among women who participate in foreign labor migration. This paper tries to fill this research gap by assessing the prevalence of spousal violence against women in overseas employment by using survey and interview data from a non-probability sample of 138 returnee women migrant workers (WMWs). The data used in this paper was collected in Dhading and Rupandehi districts in 2017 by a team of researchers, including the authors of this paper, for a comprehensive study on gender-based violence against WMWs. The study found the rate of lifetime physical violence among WMWs ten percent higher than the national average of 22 percent (MOHP 2012). This rate was highest among Dalit women and women above 35. The physical and sexual violence rates were lowest among women with secondary education or above. Similarly, the rates of physical and sexual violence were lowest among the WMWs whose husbands had secondary or higher levels of education, and the rates were highest among the WMWs whose husbands did not have a formal education. The study found only a weak relationship between women’s participation in labor migration and violence from their husbands and family members. For many WMWs, the violence prevailed in the pre-migration and post-return phases, and some women had participated in labor migration due to the violence in the family itself.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".