Empowering new survivors with old lessons? Insights from the Bosnian war aftermath applied to upcoming Ukrainian post-realities
Bibliographic record
Abstract
In the wake of the Russian invasion of Ukraine in February 2022, media narratives drew stark parallels with the violent disintegration of the former Yugoslavia, instigating discussions on historical similarities and current sociopolitical relations. This paper explores the responses of Bosnian war rape survivors and activists to the gender-based violence during the invasion and draws insights or potential lessons learned for application in Ukraine. As survivors and activists advocate for documentation and warn of the prolonged journey to justice, they navigate triggering narratives reminiscent of Bosnia’s past. Utilizing qualitative content analysis of English and Bosnian-Croatian-Serb language media articles from March to December 2022, the study identifies recurring themes and patterns, referencing the Bosnian conflict as a point of comparison. Using “post” as a temporal and analytical framework, the focus narrows to trauma narratives, the dynamics of breaking the silence, and the distinctive feature of mass digitization in the Ukrainian context. The empowerment of survivors through digital platforms is explored, shedding light on the evolving nature of information dissemination. The paper aims to provide a foundation for understanding the challenges and opportunities for post-conflict recovery and gender trauma healing in the Ukrainian context, based on social factors rather than clinical ones.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".