Whitening the inner city: the containment of Toronto's degenerate spaces and the production of respectable subjects
Bibliographic record
Abstract
The production and regulation of subjects is a spatial process. Subjects and spaces are produced in a dialectical relationship that is implicated in the constitution of racial hierarchies. Boundary demarcation is central to the spatialized production of respectable subjectivity and provides justification for the containment of racialized and sexualized bodies and spaces. This process requires and sustains a story of the city as an endangered place rescued by respectable white people. Through an examination of three moments of creating the middle class and the white city, this thesis argues that spatial management produces respectable and degenerate subjects and spaces. First, I read accounts of industry and domestic life forged in downtown Toronto in the early 1900s in order to establish how this story of origins requires regulation that constitutes and fortifies an apparently impermeable boundary. I argue that this boundary functions to divide people according to a hierarchy of social difference. The second moment is a 1977 campaign to "clean up" Toronto's Yonge Street and make it commercially and morally reputable; in the process of the "clean up," homosexual bodies were marked as degenerate, and white politicians were positioned as the saviors of respectability. The final moment examined is the late-twentieth-century marketing of converted industrial spaces in Toronto by recourse to narratives of a wasteland rescued by liberal middle class white people. These three examples represent moments in which racial and sexual hierarchies were enlisted and sustained, through discourses that bound together subjects and spaces. Such moments reaffirm a Canadian national story of whiteness as superior by virtue of its benevolence and civility, and of white people as entitled citizens. I suggest that national narratives of goodness, progress and liberalism function by displacing and erasing marginalized people from the official story of the city's making, thus further disenfranchising them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.044 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".