The achievements and challenges of women in combat: a comparative study of Israel and Canada
Bibliographic record
Abstract
In 1993, the recognized Oxford scholar John Keagan made the following assertation in his book A History of Warfare: “Women, however, do not fight. They rarely fight among themselves and they never, in any military sense, fight men. If warfare is as old as history and as universal mankind… it is an entirely masculine activity”. Almost 30 years have passed after combat roles were opened to women in at least 25 military institutions around the world and still, the mere presence of women in combat duty keeps being challenged. This investigation asks, what achievements and challenges do women face while serving in military combat and combat-support positions? This research is expected to contribute to the studies of women’s representation, recognition, and visibility in warfare, defense forces, and strategic studies with the analysis of women’s integration into military combat units. My research fills a void in Feminist IR theory inclined to view the women combatants based on the barriers and abuses they have experienced while serving in the military. I demonstrate and explore women’s unsuccessful and successful incorporation into the armed forces of two states: Israel and Canada - democratic industrialized nations that have enforced defence policy shifts to actively incorporate women into combat roles as earlier as 2000-2001.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.037 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".