Mihaela Precup & Dragos Manea - Empathy, Fantasy and the Framing of the Perpetrator in Nina Bunjevac’s Bezimena
Bibliographic record
Abstract
02/07/2020 11:00 Room 2 #empcgs Serbian-Canadian cartoonist Nina Bunjevac’s third book, Bezimena (2018), zeroes in on the perspective of a perpetrator of child sexual abuse and murder while taking on the logic of fantasy and relying on well-circulated classical myths in order to frame a narrative of sexual violence, seemingly outside the traditional confines of history and biography. In this paper, we are particularly interested in the role of empathy for the perpetrator that the graphic narrative might generate, and how an ethics of empathy might shape both our experience of the work itself and our larger moral and political (re-)actions. In conversation with scholars who expand the narrow category of “perpetrator” (Michael Rothberg, Scott Strauss), we attempt to give answers to questions such as: How can graphic narratives contribute to a more nuanced understanding of perpetration, particularly in the case of sexual assault? How do they contribute to the representation of perpetration, particularly when the depiction of perpetrators is mixed with elements of fantasy? What is the benefit of producing an ethics of empathy, wherein the perpetrator is both humanized and even made to appear sympathetic? How can we consider the gendered dimension of perpetration without simply reiterating a critique—however valid—of traditional masculinity and femininity?
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".