Violence reported by asylum seekers assisted by the Archdiocesan Caritas of Rio de Janeiro from 2010 to 2017
Bibliographic record
Abstract
Abstract Currently, the world has 89.3 million forcibly displaced people, including 27.1 million refugees. Among the reasons for displacement are torture and other forms of violence, but the real prevalence of violence before and during migration is poorly reported. The aim of this study is to analyze the prevalence of reported violence among asylum seekers in Rio de Janeiro and its associated factors. We collected secondary data from individuals who filled out the National Committee for Refugees’ asylum application forms from 2010 to 2017 and responded to the social interview at Cáritas-RJ. We included 1,546 asylum seekers with a mean age of 30 (range 15-72), 65% of whom were men. One third reported experiencing violence before arriving in Brazil. Chances of experiencing violence were 20 to 40 times higher among refugees arriving from Pakistan, Congo, Colombia, the Democratic Republic of Congo and Guinea. Physical violence/torture and psychological threats were the most frequent forms (10%, 7% and 6% of the population, respectively). Among women, sexual violence was the most frequent form of violence (9% of women). We conclude that asylum seekers in Brazil frequently suffered violence before their arrival, particularly some groups. This needs to be addressed when providing services to this extremely vulnerable population.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".