The historical and geographical patterns of the provisions for children in world peace agreements since 1990.
Bibliographic record
Abstract
The objective of this study is to provide a historical and geographical analysis that contributes to the dialogue about the impact of war in children and the actual role of negotiators, mediators, and organizations in the mitigation of these violations. It analyses 1518 world peace agreements, ceasefire agreements, and other related documents such as unilateral declarations regarding peace negotiations signed within the period of 1990 and 2016 publicly available by the Political Settlements Research Programme at the University of Edinburgh. My argument is that despite the recurrent high-level political debate to have these inclusions for children, the reality shows otherwise. The majority of peace documents (83%) and peace processes (62%) showed a considerable lack of provisions for children. While the references to children in peace documents illustrate a steady growth rate over the years, it still is considerably low. Peace Agreements around the globe, like Colombian Peace Agreement in 2016 and the Pact on Security, Stability, and Development in the Great Lakes Region in 2006 are example efforts toward disarmament, demobilization, reintegration programs, repatriation and resettlement, restoration of education for children, and promotion of health care, criminalizing and punishing acts of sexual character both in times of peace and in times of war, in accordance with national laws and international criminal law as a mechanism of prevention. We claim that is not about introducing anything new, rather than making sure that child protection provisions are a regularly occurring aspect of all peace negotiations.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".