Citizen Monsters: Race and Cannibalism in Suzette Mayr’s <i>Venous Hum</i>
Bibliographic record
Abstract
Halfway through Suzette Mayr’s 2004 novel Venous Hum, a number of the central characters are revealed to be cannibalistic vampires, some of whom are reformed and loveable while others are violent and villainous. The novel is funny and satirical with connections to cult horror films and canonical Canadian literature. By reading Venous Hum in terms of magic realism and literary cannibalism, this essay focusses on the ways in which Mayr’s evocations of vampires and cannibals lead readers towards a politicized questioning of the relationship between perceived differences and official nation-state discourse. This essay thus examines the novel’s magic realist monster imagery in relation to racialization and the politics of interpellation, visibility, inclusion, and assimilation in multicultural Canada. Mayr makes ironic use of the colonial resonances of cannibalistic discourse in order to critique the relationship between the nation-state and its varied citizens, and between official multicultural policy and the lived experience of racialization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.022 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".