‘Who were you crying for?’: Empathy, fantasy and the framing of the perpetrator in Nina Bunjevac’s Bezimena
Bibliographic record
Abstract
Serbian-Canadian cartoonist Nina Bunjevac’s third book, Bezimena (2019), embeds child sexual abuse and murder in an improbable geography where myth and fairy tale work together to create an otherworldly atmosphere, by turns mesmerizing and horrifying. Bunjevac’s previous work (Heartless [2012] and Fatherland [2014]) testifies to her continued commitment to exploring issues that are relevant to the feminist project, such as domestic violence, abortion, sexual assault and discrimination against female immigrant workers. In this article, we are particularly interested in exploring the manner in which Bezimena frames the figure of the perpetrator, as the context of the final question of the book – ‘who were you crying for?’ – repositions the entire ethical premise of the narrative by suggesting that responsibility for perpetration may lie both within and without the body and consciousness of the perpetrator himself. In conversation with scholars who attempt to expand the narrow category of ‘perpetrator’, such as Michael Rothberg or Scott Strauss, we explore how graphic narratives can contribute to a more nuanced understanding of perpetration, particularly in the case of sexual assault, and analyse Bezimena’s innovative approach to the representation of perpetration, as the book’s depiction of perpetrators and accomplices is mixed with elements of fantasy and mythology.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.049 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| 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".