Bibliographic record
Abstract
Experience has shown that the breakdown of protective structures such as families and communities, particularly in times of conflict, leaves children vulnerable to recruitment into armed groups. These children are subject to gross violations of their human rights, such as the right to protection from harm, violence and abuse. At least 300,000 children are currently being used to fight in armed conflicts in over 30 countries across the world. Girls and boys are abducted, coerced or persuaded to join armed forces, often in brutal circumstances. These children are usually involved in internal conflicts, where poverty and exclusion leave very few other viable options--becoming soldiers may appear to be their only means of survival. Many, however, sustain physical injuries and permanent disabilities as a result of combat and it is impossible to know how many are killed. A large number encounter health problems such as sexually transmitted diseases, including HIV/AIDS. Lack of data on the health of child soldiers means that appropriate medical care and treatment may be inadequate or inaccessible, even during a planned demobilization. There is an urgent need for systematic research and data collection in order to better understand and provide for the healthcare of all children leaving armed groups.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".