Bibliographic record
Abstract
introduction Globalization and Canadian LiteratureIn David Chariandy's 2007 novel, Soucouyant, the protagonist silences his black Caribbean mother's conversation with her own memories by insisting that "there are no ghosts" in the Scarborough, Ontario, neighbourhood in which they live. 1 In a sense, he is right: there have not been any ghosts in that mostly white society that they can recognize for themselves.The ghosts there are those of the white families and the Indigenous people who came before them.At the same time, the protagonist's mother, Adele, in her creeping dementia, is letting ghosts from her past in both Canada and Trinidad take on a life of their own.Trinidadian spirits like the vampiric soucouyant of the book's title are becoming, in Chariandy's narrative, part of the Canadian psychosocial landscape.Chariandy's protagonist's words are a direct echo of ones spoken by the character Mr. D --in Susanna Moodie's 1852 Roughing It in the Bush.Mr. D --states famously that "there are no ghosts in Canada" because "the country is too new for ghosts." 2 Mr. D --means, of course, white ghosts, those of the settler community that is beginning to clear the lands through which Moodie travels.In a dialogue that David Chariandy was generous enough to conduct with me about his novel, he acknowledged
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.044 | 0.032 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".