Reliable knowledge claims on the recruitment and use of children: An empirical perspective
Bibliographic record
Abstract
Abstract The risks of child recruitment by non-state armed groups are geographically, temporally and contextually situated. There are multilayered, multivariate arrays of risk factors associated with non-state armed groups, with conflicts, and with contexts. Using Bayesian network modelling with a global dataset of non-state armed group child recruitment practices between 2010 and 2022, we demonstrate the theoretical and practical importance of adopting a situational perspective to understand child recruitment risks. Methodologically, we demonstrate a robust model-checking process that checks the adequacy of our data, the magnitude and direction of estimated effects, and shows greater than 80% accuracy in predicting child recruitment by non-state armed groups. We review and contrast our approach with standard general linear modelling used in quantitative child recruitment research over the past two decades. Through adopting a situated orientation, and applying analytical tools appropriate to that orientation, we challenge and extend existing theory and propose new theoretical insights on child recruitment risks. We show how important violence is as a predictor of child recruitment risks and, using a new measure of fighting force efficacy, show that, contrary to published theory, less effective non-state armed groups were more likely to recruit children than more effective ones. But even these most notable results we show to vary markedly across situations.
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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 | 0.061 | 0.307 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".