Guidance on Protection from Sexual Exploitation and Abuse
Bibliographic record
Abstract
Sexual exploitation and abuse (SEA) by humanitarian workers are some of the most serious failures of protection and are grave violations of the responsibility of humanitarian personnel to do no harm. Whereas instances of SEA are not a new occurrence, the pervasiveness of this phenomenon was only exposed to the public eye in 2002 following allegations of SEA by aid workers against refugees and displaced women and children in West Africa. Acknowledging that the problem was global, the Inter-Agency Standing Committee (IASC) Task Force on Protection from Sexual Exploitation and Abuse (PSEA) was established in 2002 with the short-term purpose of clarifying core standards of conduct and establishing practical measures to address misconduct perpetrated by United Nations (UN) staff and affiliated humanitarian and development personnel. In 2003, the United Nations Secretary-General’s Bulletin Special measures for protection from sexual exploitation and abuse was released and became a landmark document outlining the UN’s zero-tolerance approach to SEA. One of the key elements of the bulletin was the requirement of mandatory reporting, which obliges UN staff and implementing partners to immediately communicate any concern or suspicion of SEA by colleagues through established reporting mechanisms. Meanwhile, international organisations quickly took steps in the same direction, both individually by adopting codes of conduct, reporting systems and investigation mechanisms, and collectively through coordination groups and other initiatives.4
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.019 | 0.013 |
| Insufficient payload (model declined to judge) | 0.019 | 0.012 |
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".