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Record W4283582934 · doi:10.3126/mef.v12i01.45891

Women's Participation in Foreign Labour Migration and Spousal Violence: A Study on Returnee Women Migrant Workers in Nepal

2022· article· en· W4283582934 on OpenAlexaff
Arjun Kharel, Amrita Gurung

Bibliographic record

VenueMolung Educational Frontier · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsConcordia University
Fundersnot available
KeywordsSexual violenceDomestic violenceMigrant workersDemographyPsychologyPoison controlInjury preventionMedicineSociologyEconomic growthEnvironmental healthCriminologyEconomics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.304
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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