Bibliographic record
Abstract
This article argues that systematic rape should be conceptualized not only as a war crime, but also as a destructive and increasingly deployed war weapon. As such, rape becomes a subject of arms control and thus directly relevant to security studies. Consequently, I argue that international relations should consider rape as a weapon of war for two major reasons. First, the categorization of rape as a weapon of war fits with core disciplinary theoretical definitions and assumptions. Namely, rape as a weapon of war compromises state security, operates in a conception of power defined as material/“power-over”/zero-sum, and corresponds with a rational actor model. Second, although wartime rape has often been marginalized as a “women's issue,” empirical evidence persuasively demonstrates how this categorization is incomplete; rather, women, girls, men, and boys all suffer direct and/or indirect consequences from the increasing prevalence and brutality of this weapon's deployment. Overall, the article maintains that excluding rape from security studies precludes comprehensive, accurate analysis within areas of theoretical and practical concern to IR. Thus, I conclude by suggesting avenues of research, from diverse theoretical perspectives, that may persuade IR scholars to view rape as an increasingly relevant and analytically rich topic of study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".