Why Women Rebel: Understanding Women's Participation in Armed Rebel Groups
Bibliographic record
Abstract
Why Women Rebel" presents a global analysis of the extent to which women are engaged in armed, organized rebellions, and why they choose to join such rebellions. Henshaw has collected and analyzed data on women's participation in over 70 post-Cold War rebel groups and provides a theoretical analysis drawing upon both mainstream literature in the social sciences and critical, feminist inquiry on women and political violence to offer a new gendered theory on why women rebel. The book demonstrates that women are active in well over half of all rebel groups sampled and that, while the majority of rebel groups have women serving in support roles away from direct combat, approximately a third of groups employ women in the conduct of armed attacks, and just over a quarter have women in a leadership capacity. Henshaw reaffirms the idea that women are more likely to be engaged in left-wing political organizations, but does suggest that more conservative or traditional movements may also successfully incorporate women by appealing to concerns about community rights.0This book will be of interest to academics in the fields of political science, international relations, security studies, and gender and women's 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".