Breaking the Risk of Conflict Trap: Way forward for Limiting Child Soldiers’ Phenomenon
Bibliographic record
Abstract
The recruitment and use of children in armed conflicts or child soldier phenomenon has inflicted much pain and suffering to thousands of children in many countries during recent decades. This article examines the pathway to protect children from predatory recruitment as well as the prevention of those who are already recruited from becoming trapped into further violence, generically termed as ‘conflict trap.” In pursuance, the current law shall be applied as an instrument of social engineering to bring about the desired change towards the protection of child soldiers alongside the use of art and science of persuasion in promoting the ideas. Therefore, to accomplish the above desired social change, this article proposes three objectives: firstly, to explain the law in plain terms and present it as a valuable instrument designed to protect children in armed conflict situations; secondly, to enumerate the serious psycho-social impact to the mental health of the child, and the ensuing disruption of the cognitive and affective development of the child, that are often irreversible; thirdly, to present reintegration program that has transformed former child soldiers into international figures like Ishmael Beah. It is premised that these three objectives, if implemented in society, would be successful strategies in imputing the desired social change, which is to rally support for the elimination of child soldiering, thus diminishing some incentives for conflict perpetuation. Therefore, these strategies would help to prevent the recruitment and use of children in armed conflicts and in the long term will aid in the breaking away from conflict trap. As child soldiers in previous conflicts will be ready to resume fighting, limiting the impact of conflict traps becomes imperative to preserve a conflict free society.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".