Children and Violence : Agency, Experience, and Representation in and beyond Armed Conflict
Bibliographic record
Abstract
This multi-disciplinary volume provides an innovative approach to children and violence, looking beyond the existing literature that focuses on child soldiers in the 'Global South'. Harnessing expert contributions from over a dozen countries, the book examines the relationship between children and violence, with a focus on children ensnared in military conflict, embroiled in criminal gangs, and enmeshed in political activism. It analyses how children join fights, how they fight, and what happens to them after fighting officially ends. It addresses cutting-edge issues such as cyberwars, self-defence, intergenerational trauma, gender fluidity, racism, and state surveillance. Throughout, the book underscores the need to respect the agency and dignity of children and youth, to build cultures of juvenile rights, and to think critically of the place of the child amid global power politics and decolonialization. Through accessible writing, and the provision of considerable new data, this book supports advocacy work, and will enrich teaching and spark further academic research. This book will be of great interest to students of International Law, Human Rights, Childhood Studies, International Relations, Peace and Conflict Studies, Post-conflict studies and Security Studies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".