The child casualties of war — A scoping review of the tools used to report and monitor grave violations of children’s rights in situations of armed conflict
Bibliographic record
Abstract
During armed conflict, children are at an increased risk of experiencing grave violations of their rights.1-6 In accordance with the United Nations there are six primary grave children’s rights violations during conflict; (1) killing or maiming of children, (2) recruitment or use of children by armed forces or armed groups, (3) attacks on schools or hospitals, (4) rape or other sexual violence against children, (5) abduction of children, (6) denial of humanitarian access to children.7 Early efforts to establish an international mechanism to report and monitor grave violations against children in conflict have faced a multitude of barriers—including lack of capacity, subjectivity in reporting, and political intimidation—which challenge the validity and accuracy of reports and have led to an inefficient, fragmented and the ill-coordinated methodology of data collection.8,9 Evidence-based action is promoted as the foundation of policy responses within the current geo-political climate; yet there is little description of published work which describes reporting instruments and monitoring mechanisms specifically aimed at collecting data on violations against children in armed conflict.10,11 The following study aims to explore the existing body of research and grey literature related to data collection methods implemented to monitor and report grave violations of children’s rights in armed conflict.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".