Bibliographic record
Abstract
Writing in the Literary History of Canada almost thirty-five years ago, Alec Lucas noted that "nature writing, particularly the animal story, had its hay-day in the late nineteenth and early twentieth centuries. It has long past. Perhaps the literary vein has been worked out" (1965:404). In retrospect, Lucas's epitaph for animal presences in Canadian literature may be premature; writing in the same volume, no less a lumi- nary than Northrop Frye noted, with no predictions for the genre's demise, "the prev- alence in Canada of animal stories, in which animals are closely assimilated to human behaviour and emotions" (343). Indeed, according to Gaile McGregor, any survey of the foundational works of Ernest Thompson Seton and Sir Charles G.D. Roberts and the many animal imaginings offered by recent and contemporary authors reveals that "Canadians are fascinated by animals" (1985: 192). Poets as diverse as Layton, Atwood, Ondaatje, Page, Lane, Acorn, and Nowlan (among others) have written of animals; novelists such as Fred Bodsworth, Sheila Watson, Marian Engel, and Thomas King have all created animal characters of one form or another. The proliferation of an increasingly urbane literature in the Canadian imaginative landscape may bear out Lucas's prediction for the demise of the animal genre at some future date, but for now the important position of non-human actors in the recent history of the Canadian imagination remains intact, a fact that has been further emphasized by the recent crit- ical and commercial success of Barbara Gowdy's elephant novel, The White Bone.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.060 | 0.025 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".