Comparing migration experiences of Venezuelan women and girls: a mixed-method, cross-sectional analysis of refugees/migrants in Ecuador, Peru and Brazil
Bibliographic record
Abstract
Objective: Over the past decade, geopolitical turmoil in Venezuela has resulted in the displacement of 7.1 million people, resulting in a migration and refugee crisis. Methods and analysis: This current cross-sectional, mixed-method research, which is focused on women and girls, examines differences in Venezuelan refugee/migrant demographics, migration characteristics, experiences and perceptions across nine locations in Ecuador, Peru and Brazil. Results: A total of 9116 Venezuelan refugees/migrants shared 9339 migration experiences. Respondents in Brazil had been displaced for less time, reported more extreme poverty, perceived that they had received more support from the host community, and had more positive migration experiences. In contrast, respondents in Peru had been displaced for longer, were more likely to share experiences of insecurity/violence, perceived that they had not received adequate support and were more likely to report that the migration experience was negative. Respondents in Ecuador tended to provide more moderate responses somewhere between those from Brazil and Peru with one exception being around the impact of COVID-19, which they perceived more negatively. Conclusion: It is critical to recognise that Venezuelan refugee/migrant populations are not homogenous and that their experiences, needs and priorities vary by location of settlement and migration route. From these findings, we recommend more open regularisation policies for Venezuelan nationals in Ecuador and Peru in addition to improved socioeconomic integration in accordance with the Quito Process. Sharing of successful models from other contexts may prove helpful.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".