Who is a Victim? Difference and Accountability in Shani Mootoo’s Cereus Blooms at Night
Bibliographic record
Abstract
Contributing to what Dominick LaCapra has identified as the institutionalization of trauma studies in the humanities, Margaret Atwood once identified survival, colonization, and hardship as the primary experiences of “Canadian-ness.” Many writers have since exposed fundamental flaws in this model. Shani Mootoo’s novel Cereus Blooms at Night provides clear examples of the ethical problems formed between witness and victim within an overarching framework of victimization. Here, perpetrators identify as victims, victims identify as perpetrators, and accountability becomes a blur. The narrator’s piecing together of fragmented memories and utterances problematically integrates multiple accounts into a single overarching voice that equates traumatic experience with a repetition of victimization. By crafting a narrative voice that is problematic, monologic, and ultimately appropriating, Mootoo illuminates weighty issues that strain the fabric of “trauma studies,” as well as social and political life more generally. She thus calls for a remapping of ethics in what Annette Wieviorka has described as “the era of the witness.”
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.042 | 0.033 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".