Children's Participation in Holding International Peacekeepers Accountable for Sex Crimes
Bibliographic record
Abstract
Over the years, there have been hundreds of unresolved allegations and specifically-identified, credible cases of United Nations- ("UN") mandated or otherwise UN-authorized international peacekeepers perpetrating sex-related human rights violations and crimes against children in the country of deployment. In some situations, such as in the Central African Republic, children have been the frequent victims of these alleged sex crimes by international peacekeepers. Arguably, these crimes can rise to the level of Rome Statute-defined international crimes in some instances. 2 This paper discusses the potential for child participation in international judicial and quasi-judicial mechanisms directed towards: (i) criminal and/or civil accountability of individual international peacekeepers for sexual exploitation and abuse of children, and (ii) accountability of the troop-sending State where there is a failure of that State to properly investigate and, where warranted, prosecute their peacekeeper nationals responsible for SEA perpetrated while on a UN peacekeeping mission. This paper also highlights the tensions that can arise between (i) the child's right to participate in the pursuit of justice in seeking accountability and a remedy for their SEA victimization by international peacekeepers and (ii) the child's immediate and long-term protection needs.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".