Women's Resistance: Strength-Based Outcomes of Wartime Sexual Violence Against Women from Bosnia and Herzegovina
Bibliographic record
Abstract
Abstract: Women's wartime sexual violence experiences have largely been constructed as negative, leaving little room for women's resistance and strength-based outcomes. Using Interpretative Phenomenological Analysis, this study examines women's resistance using semi-structured interviews with 13 ethnically diverse women victimized by sexual violence during the war in Bosnia and Herzegovina. Using partial results from the study highlighting strengths that are both external and internal to the women, I show that women resist traditional discourses of victimization and gender stereotypes as they speak about the strengths they have acquired and retained after they were sexually victimized. Résumé: Les expériences de violence sexuelle vécues par les femmes en temps de guerre ont été largement considérées comme négatives, ce qui laisse peu de place à la résistance des femmes et aux résultats fondés sur l'endurance. En utilisant l'analyse phénoménologique interprétative, cette étude examine la résistance des femmes à l'aide d'entretiens semi-structurés avec 13 femmes de diverses ethnies victimes de violences sexuelles pendant la guerre en Bosnie-Herzégovine. En se servant des résultats partiels de l'étude mettant en exergue les forces qui sont à la fois externes et internes aux femmes, je montre que les femmes résistent aux discours traditionnels de victimisation et aux stéréotypes de genre lorsqu'elles parlent des forces qu'elles ont acquises et conservées après avoir été victimes de violence sexuelle.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".