Beyond the Binary: Why Gender Matters in the Recruitment and Use of Children
Bibliographic record
Abstract
Gender matters in conflict. Socio-cultural norms, attitudes and expectations related to gender dictate the causes, course and consequences of child soldiering. Despite international commitments, the recruitment and use of children in armed forces and groups persists. This paper summarizes existing quantitative data from the United Nations Monitoring and Reporting Mechanism, in light of complementary qualitative analysis from other sources, to highlight the ways in which gender norms can (a) drive recruitment, (b) determine roles and responsibilities, and (c) influence outcomes for children associated with armed forces or groups. The needs and experiences of girls and boys are explored, and where evidence allows, that of children of diverse sexual orientation, gender identity and expression, and sex characteristics (SOGIESC). Recommendations are made on potential actions that can further nuance the gender perspective proposed in the Vancouver Principles. Suggestions are made on how to ensure prevention and response interventions are (1) supported by consistently disaggregated data, (2) cognisant of the gender drivers behind recruitment, and (3) tailored to the distinct needs of children of diverse SOGIESC.
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 | 0.039 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".