The International Legal Framework To Protect Children In Armed Conflict
Bibliographic record
Abstract
The core elements of the international legal framework are strong tools for the protection of children affected by armed conflict. However, there are other tools which have not been discussed in this introduction, such as the Ottawa Convention and the European Union Guidelines on Children and Armed Conflict, which can contribute to protection.Implementation of this framework needs continued investment in terms of national legislation and allocation of sufficient resources with the involvement of the many agencies such as NGOs, the Special Representative, the Security Council and other UN agencies. This investment should not be limited to children associated with armed forces or armed groups, but instead should include the protection, recovery and reintegration of all children affected by armed conflict, as required under the ACRWC and the CRC.Special attention is given to remedies for children’s rights violations—for example, through truth and reconciliation commissions at the national and international level, and the prosecution of perpetrators of crimes against children via special courts and tribunals, and the ICC. The Third Optional Protocol to the CRC providing communications procedure can become an extra tool for remedies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.025 | 0.018 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".