Bibliographic record
Abstract
Concern is growing about the increasing use of child soldiers in armed conflicts around the world.1 However, it may not be enough to just condemn or prohibit the recruitment of children. We need to ask why children join armies. If we are to prevent children fighting we need to understand the conditions under which children become soldiers and work to improve these conditions. One such context, that of Sri Lanka, may shed some light on the issues. The reasons why children become fighters can be categorised into push and pull factors. The use of push-pull categorisation has been used recently in relation to child labour by the International Labour Organization (see www.ilo.org/public/english/standards/ipec/child/2tour.htm) and more specifically child soldiers (see http://www.child-soldiers.org/conferences/confreport_asiawgc.html).2 #### Summary points The recruitment and use of children as soldiers should be condemned and prohibited Understanding why children choose to fight is important for preventing it Factors that prompt children to join armed groups include witnessing the death of relatives; destruction of homes; displacement; economic difficulties; political oppression, and harassment Children may be enticed by beliefs in the cause, threat to group identity, propaganda, thrill of adventure, and entrapment Responsibility lies not only with those recruiting children but also with the civil society, state, and international community ### Traumatisation In the civil war that has been in progress in north east Sri Lanka for almost two decades children have been traumatised by common experiences such as shelling, helicopter strafing, round ups, cordon and search operations, deaths, injury, destruction, mass arrests, detention, shootings, grenade explosions, and landmines. Studies focusing on children in war situations—for example, in Mozambique3 and the Philippines4—report considerable psychological sequelae. A detailed Canadian study of children in the Eastern Province of Sri Lanka found considerably more exposure to war trauma and psychological sequelae in ethnic minority Tamil …
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.034 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 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".