Thinking against trauma binaries: the interdependence of personal and collective trauma in the narratives of Bosnian women rape survivors
Bibliographic record
Abstract
In this article, we draw on feminist trauma studies with the aim of deconstructing the theoretical and methodological binary between individual and collective trauma. Based on first-hand interviews with Bosnian survivors of rape, we attempt to ‘think against’ the private/public split that trauma studies work often unintentionally reifies. We draw upon recent methodological innovations that have been influenced by thinkers such as Derrida and Deleuze. Specifically, we work with what Jackson and Mazzei call rhizomatic and trace readings in the threshold. Through a rhizomatic and trace reading of narrative pieces extracted from the interviews, we engage with the following questions: 1) How do we theorise what Davoine and Gaudilliere call ‘the sociopolitical faultlines’ between collective/public accounts of trauma and those traditionally constructed as private/personal? 2) How do accounts of war rape, which narrate the eruption of the past into the present, elucidate the myriad links between the private and public in a number of ways; among others, the echoes or traces of the everyday ‘before’ in subjects’ stories of the monstrous ‘after’? And 3) What is the relationship between the ‘unspeakable’ in the traumatic memories of the survivors and the ‘speakable’ collective memories of traumatic humanmade events? How does the collective desire ‘not to know’ or ‘to forget’ impact on the individual survivor’s ability to reconstitute their post-trauma identity in a personal as well as a social context? The aim of the analysis is to show that the multifaceted nature of the traumatic reality demands a multifaceted approach that resists binary constructions relating to self/other, private/public, individual/collective.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.043 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| 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".