Bibliographic record
Abstract
Robert Fulford called it “a remarkable glimpse of the underbelly of Toronto,” but the reviews that greeted the publication of Cabbagetown Diary in 1970 were decidedly mixed. The novel’s rowdy concoction of grit and violence and rooming-house sleaze had a strongly polarizing effect on its readers. Many admired the frankness of Butler’s depiction of a sordid environment, and others deplored the obscenity of the language and the dangerous and careless ways in which his characters behave, bent as they are on downward self-transcendence. But Cabbagetown Diary was undeniably a promising debut by a young writer whose brash tone and pungent subject matter were unique in Canadian writing at that time. The novel takes the form of a diary written by a disaffected young Toronto bartender, Michael, over the course of his four-month liaison with Terry, a naive teenager who is new to the city. Michael introduces her to his friends and his inner-city haunts, to drink and drugs, and to the nihilist politics espoused by some in his circle. With hard-bitten cynicism and flashes of dark humour, Michael relates the vicissitudes of their summer together. This reissue of Cabbagetown Diary includes a biographical sketch by Charles Butler and an afterword by Tamas Dobozy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.198 | 0.047 |
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".