It’s time to address sexual violence in academic global health
Bibliographic record
Abstract
Looking back over the first year of the global #MeToo movement that brought sexual violence (from harassment to coercion and assault) out in the open,1 we note that this issue has received little attention in academic global health. Recent cases of sexual misconduct in Joint United Nations Programme on HIV and AIDS (UNAIDS)2 and Oxfam show that the global health community must act to address this problem.3 In just a few months, the #MeToo movement raised awareness, stimulated new debates and placed this issue squarely on the public agenda in politics, business and entertainment. But academic global health still does not adequately prepare (women and men) students and academics for this problem; and recent discussions on global health training ignore the problem.4 Indeed, there is an urgent need to implement evidence-based comprehensive and integrated prevention strategies to address sexual violence in global health academic research.5 Collaboration between partners from countries with unequal incomes and power is common in academic global health.6 7 While challenges of power, money, publication, data use, and so on, within such collaborations are widely discussed,6 8–10 there is relative silence around sexual violence. Gender inequality in global health is increasingly discussed11—as scientific panels are often composed of a majority of (or only) men and the work of unpaid young women represents a large proportion of global health internships.12 There is a new movement to highlight women leaders in global health,13 and junior women researchers are calling to depatriarchalise science for French-speaking women.14 Despite these discussions, the general lack of consideration for sexuality issues in research fieldwork remains.15 16 In 1985, Gurney stated, “ for female fieldworkers, reciprocity… may be problematic if powerful males in the setting expect sexual favors in return for research …
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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 | 0.038 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.037 | 0.054 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.035 | 0.043 |
| Insufficient payload (model declined to judge) | 0.071 | 0.021 |
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".