Bibliographic record
Abstract
Trauma theorists foreground the unrepresentability of trauma; however, with modern innovations in visual representation, such as the photograph and cinema, depictions of trauma have begun to circulate across different mediums for a variety of audiences. These images tend to problematically present the traumatic event rather than the effects of trauma, such as traumatic memory. Specifically, some contemporary Hollywood popular films and television series that include rape as their subject matter often include a rape scene that can evoke affects such as disgust or empathy, and while these affects can last the duration of the film, they fail to shift popular discourses about rape because affect is more productive when it focuses on effects instead of events. As trauma studies has shifted to memory studies in the Humanities, and rape has become more prominent in popular culture through the circulation of personal testimony on social media and memoir, depictions of rape in cinema have slowly started to change from presentations of rape scenes to representations of rape trauma that highlight different affects, such as shame. Using Monster (2003), Girl with the Dragon Tattoo (2011), Room (2015), and the television series, 13 Reasons Why (2017) and Sharp Objects (2018) as case studies, this paper argues that, for an audiovisual depiction of rape to shift popular discourses about rape, it would have to function rhetorically to widen the cultural understanding of rape trauma beyond the event, and demonstrate that rape trauma should be understood as part of the personal, unconscious, cultural, and visual mediation of traumatic memory.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".