Bibliographic record
Abstract
‘Is a child still a child when pressing the barrel of a gun to your chest?’ asks Lieutenant-General (Ret’d) Roméo Dallaire (Dallaire 2010: 2). Dallaire served as Force Commander of the peacekeeping United Nations Assistance Mission for Rwanda (UNAMIR), where he was first confronted with child soldiers during the genocide of 1994. He then went on to found the Roméo Dallaire Child Soldiers Initiative to end the use of child soldiers in conflict. Children should not know or be involved in war. With his question, however, Dallaire points to an important and disturbing concern in conflict and in post-conflict social reconstruction: child soldiers are a paradox that confuses our understanding of terms like childhood, perpetrator, and innocence. This paradox makes the issue of post-conflict justice even more complex: when children do participate in conflict, when they commit horrific acts of violence, when they later come face to face with the victims of their violence, when they themselves are likely victims of abuse and violence, when they need to reintegrate into a peaceful and well-ordered society, and when they are needed to contribute to its rebuilding and to flourish as members of the society’s future, how should child soldier perpetrators of abuses be perceived and treated? These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.186 | 0.074 |
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".