Sarah Percy. <i>Forgotten Warriors: The Long History of Women in Combat</i>.
Bibliographic record
Abstract
Sarah Percy’s new book, Forgotten Warriors, issues a call to arms against a fresh generation of authoritarians and patriarchists who have targeted women’s newly gained rights (at least in Western militaries) to engage in combat. Partly for this reason, the book has already been reviewed widely. Percy, an academic at the University of Queensland, makes her case by appealing to history, although this history is not the stuff of conventional wisdom. Rather, the author takes a revisionist approach to military history, arguing that women in the past had “survived and even thrived as part of a machine of war” (xv), although news of this realty was suppressed and has still not been conceded by many military historians (John Keegan is the author’s main target). Percy does not frame this erasure as a conspiracy, but rather the consequence of long-standing chauvinist patterns of authority, which she describes as “the playbook of patriarchy” (xvi). As Percy notes, the manifest ability of women to succeed in combat has only recently been acknowledged, allowed, and valorized. She admits that historically women have not “fought in the same numbers and in the same ways as men” (xiv), but she attributes this mainly to men preventing them from getting a chance to perform in combat, especially after the 19th century. The takeaway, according to Percy, is that through the longue durée women did succeed in proving their ability in combat, and consistent examples of their presence on the battlefield made female warriors more than exceptions to a general rule. She also condemns political and military establishments for trying—not always successfully—to keep women out of combat, thereby denying them a way to demonstrate “the virtues that we associate with success in society, politics and . . . just about every field of human endeavour” (284–85).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".