‘First and foremost a writer of fiction’: revisiting two Toronto novels, Hopkins Moorhouse’s <i>Every Man for Himself</i> and Peter Donovan’s <i>Late Spring</i>
Bibliographic record
Abstract
Hopkins Moorhouse and Peter Donovan (or P.O’D.) were once familiar names in Canadian literature. In the first decades of the twentieth century both authors wrote a variety of sketches and stories for Canadian magazines and newspapers, and went on to produce well-received, popular, Toronto-set novels. The intervening years have seen both writers and their novels all but forgotten. This article revisits Moorhouse’s Every Man for Himself (1920) and Donovan’s Late Spring (1930) in light of an increasing interest in the depiction of cities in Canadian literature. Both novels can be seen as self-aware modern urban Canadian fictions, addressing the complexity of the cityscape alongside the overarching challenges of modernity to literary representation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.029 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".