Bibliographic record
Abstract
At War with Women reveals how post-9/11 politics of gender and development have transformed US military power. In the mid-2000s, the US military used development as a weapon as it revived counterinsurgency in Iraq and Afghanistan. The military assembled all-female teams to reach households and wage war through development projects in the battle for "hearts and minds." Despite women technically being banned from ground combat units, the all-female teams were drawn into combat nonetheless. Based on ethnographic fieldwork observing military trainings, this book challenges liberal feminist narratives that justified the Afghanistan War in the name of women's rights and celebrated women's integration into combat as a victory for gender equality. Jennifer Greenburg critically interrogates a new imperial feminism and its central role in securing US hegemony. Women's incorporation into combat through emotional labor has reinforced gender stereotypes, with counterinsurgency framing female soldiers as global ambassadors for women's rights. This book provides an analysis of US imperialism that keeps the present in tension with the past, clarifying where colonial ideologies of race, gender, and sexuality have resurfaced and how they are changing today.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.088 | 0.029 |
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".