<i>Rouge</i> : The Subway Poetics of Adrian De Leon’s Sub/Urban Toronto
Bibliographic record
Abstract
This article draws on theories of Canadian suburban literature (Cheryl Cowdy 2022) and suburban settlement (Zhixi Zhuang 2024), alongside experimental and subway poetics (Eric Schmalz 2022; Tim Conley 2014), to explore the Canadian suburban poetic imaginary. Focusing on Adrian De Leon’s 2018 collection Rouge: Poems, which maps a subway journey from western Toronto to the eastern suburb of Scarborough, the article examines how poetry serves as both a lyrical and rhetorical narrative, interrogating the relationship between city and suburb, particularly the suburb’s role as the city’s shadowed double. Ultimately, the article argues that Rouge: Poems uses lyrical emotion, play, humor, and satire to depict the suburban and subway settings not just as thematic backdrops, but as active, generative forces that question and reshape urban–suburban relationships, expanding our knowledge of suburban and subway poetics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".