Bibliographic record
Abstract
"This anthology is breathtaking in its geographic and temporal sweep." Canadian Journal of History The American media has recently "discovered" children's experiences in present-day wars. A week-long series on the plight of child soldiers in Africa and Latin America was published in Newsday and newspapers have decried the U.S. government's reluctance to sign a United Nations treaty outlawing the use of under-age soldiers. These and numerous other stories and programs have shown that the number of children impacted by war as victims, casualties, and participants has mounted drastically during the last few decades. Although the scale on which children are affected by war may be greater today than at any time since the world wars of the twentieth century, children have been a part of conflict since the beginning of warfare. Children and War shows that boys and girls have routinely contributed to home front war efforts, armies have accepted under-aged soldiers for centuries, and war-time experiences have always affected the ways in which grown-up children of war perceive themselves and their societies. The essays in this collection range from explorations of childhood during the American Revolution and of the writings of free black children during the Civil War to children's home front war efforts during World War II, representations of war and defeat in Japanese children's magazines, and growing up in war-torn Liberia. Children and War provides a historical context for two centuries of children's multi-faceted involvement with war.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".