From Youth Affected by War to Advocates of Peace, Round Table Discussions with Former Child Combatants from Sudan, Sierra Leone and Cambodia
Bibliographic record
Abstract
There are a roughly estimated 250,000 children serving as combatants in armed groups worldwide. They are forced to perpetrate horrific violence and subjected to the same. Studies on the impact of the use of children in armed conflict have tended to focus on the demographics, roles and mental health outcomes of this population and programs are centered on rehabilitation. Few programs, however, are focused on mitigating access, stopping recruitment and securing the release of child combatants during the thick of the conflict or in its immediate aftermath. These interventions are desperately needed not only to ensure the protection of children, but also to help stop conflict and insecurity. In order to gain insight into what more can be done, particularly by security forces, to prevent and ideally halt the practice of using children in combat, the Child Soldiers Initiative hosted a two-day Round Table meeting in Halifax Canada with former child combatants from Sierra Leone, Sudan and Cambodia, academics and humanitarian non-governmental organizations to examine this issue. The results of this meeting are summarized here and include recommendations made to communities, humanitarian organizations, United Nations and peacekeeping forces that address strategies for reducing access and mitigating the use of children in combat.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".