Migration, violence, and safety among migrant sex workers: a qualitative study in two Guatemalan communities
Bibliographic record
Abstract
Despite reports of high levels of violence among women migrants in Central America, limited evidence exists regarding the health and safety of migrant sex workers in Central America. This study is based on 16 months of field research (November 2012-February 2014), including ethnographic fieldwork, in-depth interviews, and focus groups conducted with 52 internal and international migrant female sex workers in Tecún Umán and Quetzaltenango, Guatemala, key transit and destination communities for both international and internal migrants. The analysis explored migration-related determinants of susceptibility to violence experienced by migrant sex workers across different phases of migration. Violence in home communities and economic considerations were key drivers of migration. Unsafe transit experiences (eg undocumented border crossings) and negative interactions with authorities in destination settings (eg extortion) contributed to migrant sex workers' susceptibility to violence, while enhanced access to information on immigration policies and greater migration and sex work experience were found to enhance agency and resilience. Findings suggest the urgent need for actions that promote migrant sex workers' safety in communities of origin, transit, and destination, and programmes aimed at preventing and addressing human rights violations within the context of migration and sex work.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".