Feminist Debates on Civilian Women and International Humanitarian Law
Bibliographic record
Abstract
International humanitarian law [IHL] provisions address the situation of civilian women caught in armed conflict today, but is this law enough? Feminist commentators have considered this question and have come to differing conclusions. This article considers the resulting debate as to whether female-specific IHL provisions are adequate but underenforced, or inadequate, outdated and in need of revision. One school of thought argues that the main impediment to the protection of female civilians during hostilities is lack of observance of existing IHL. A second school of thought believes that something more fundamental is needed to meet the goal of protecting civilian women during war: revision and reconceptualization of IHL to take into account systematic gender inequality. This article considers the status of this debate within three areas of IHL considered by many to be central legal aspects of the experience of female civilians caught in armed conflict: the general non-discrimination provisions, the specific protection for civilian women against sexual violence and the specific protection of pregnant women and mothers. It concludes that, while there has been a vibrant debate within feminist circles on the adequacy of existing IHL provisions, mainstream action has tended to focus on enforcement. This is unfortunate, as it means that certain insights into the impact of deep gender inequalities on conflict have largely been left unexplored.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| 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".