Bibliographic record
Abstract
Early warning of the recruitment and use of child soldiers remains an elusive concept. This is surprising given the number and intensity of conflicts today where child soldiers are used. Yet, there is currently no formal early warning system in this sphere that focuses on recruitment and use. Without formally looking at indicators that precede recruitment, the international community runs the risk of missing important opportunities for data collection and analysis which could help to improve child protection and inform conflict mitigation. This paper will employ a qualitative review of the policy and research domains to examine the current landscape of early warning as it applies to child soldiers. It will consider why it is important to expand the scope of early warning to incorporate recruitment and use, so that children can be prioritized on the international security agenda and, to further understand why some children are more vulnerable to recruitment than others. Ultimately, this paper argues that the development of an early warning system for child soldiers would be important to better inform recruitment prevention from its earliest stages.Keywords: Early Warning, Child Soldiers, Conflict, Recruitment Prevention, Child Protection
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".