Risk, Mortality, and Memory: The Global Imaginaries of Cherie Dimaline’s The Marrow Thieves, M.G. Vassanji’s Nostalgia, and André Alexis’s Fifteen Dogs
Bibliographic record
Abstract
This paper examines three contemporary Canadian novels that depict global risk society through a speculative fictional form that asks the question "What if?" Cherie Dimaline's The Marrow Thieves (2017) and M.G Vassanji's Nostalgia (2016) imagine dystopian worlds ravaged by climate change to critique humanist ideals of Progress.André Alexis's Fifteen Dogs (2015) uses the animal fable to address what it means to be a mortal animal.Each asks what an awareness of risk means for agency and ethics: for Indigenous people in The Marrow Thieves; for Torontonians in the context of a heightened global apartheid in Nostalgia; and for dogs wrestling with a god-granted human intelligence in the contemporary Toronto of Fifteen Dogs.In negotiating risk, each fiction turns to the roles of memory, creativity, and alternative forms of subjectivity and community in ensuring survival.Each novel finds fragile yet necessary steps toward alternative futures in the ability to imagine otherwise.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.056 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".