Early Warning Systems and the Recruitment and Use of Children in Armed Violence
Bibliographic record
Abstract
Current wars are devastating for children, from mass deaths in bombardment in urban areas to their recruitment as soldiers by armed groups. While progress has been made on reducing the recruitment and use of children by state armed forces, with advances in international law, understanding of the phenomenon, and measures for demobilisation and reintegration, many non-state armed groups persist in the practice. This Special Issue argues that an increased focus on understanding, predicting, and preventing child recruitment, not just responding to it after it starts, is needed. There are significant bodies of literature on both early warning and child soldiering, but little work has considered them together, or how early warning and prevention perspectives can improve children’s wellbeing and sustainable peace. The issue’s articles bring together these interdisciplinary bodies of literature, speaking to recent debates on intervention, knowledge production, and methodological development in conflict research. This collection draws on multiple perspectives to address definitions, data collection, gender, norms around children and violence, existing early warning systems, and how to translate early warning into preventative action. Together, they inform scholars working on children and armed conflict, quantitative research on conflict and early warning systems, and provide insights for policymakers and practitioners.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".