UN-MENAMAIS : understanding the mechanisms, nature, magnitude and impact of sexual violence in Belgium : final report for the Belgian Science Policy Office
Bibliographic record
Abstract
Sexual violence (SV) is a major public health, judicial and societal concern in Belgium.A comparative and representative study of SV in Belgium was still lacking.The UN-MENAMAIS study aimed to contribute to a better understanding of the mechanisms, nature, magnitude and impact of sexual violence on female, male and transgender victims, their peers, offspring, professionals and society in Belgium and generate policy recommendations and prevention and response strategies.This mixed methods study showed that sexual violence is prevalent in Belgium and affects people of all ages, genders, sexual orientations and legal statuses.Being sexually victimized is linked to worse mental health outcomes across the life course.Moreover, victims find it difficult to disclose what happened, to seek professional help and to report to the police.Furthermore, doctors are not sufficiently aware of the care they should provide, nor of the potential impact of a forensic examination and do not systematically refer victims of sexual violence to adequate care, forensic or legal authorities.Regardless of the forensic model considered, victims find it difficult to gain recognition from the courts because of the judicial logic, which involves specific principles of proof and for which forensic evidence is not always sufficient. Further readingCapsule 3: Sexual violence in older adults in Belgium: https://vimeo.com/559892123/d3232ac805
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".