Queering the In-between: Liminality and Environmental Gentrification in Toronto
Bibliographic record
Abstract
This dissertation examines environmental gentrification through the lens of queer affect, looking at two parks-led redevelopment projects in downtown Toronto, Canada: Reimagine Galleria and the Green Line. In it I argue that place-specific affective dynamics and politics shape and drive environmental gentrification processes. I also argue that environmental gentrification shapes people's emotional relationships with the places they live, while altering the more-than-human relations that constitute those places. Using queer affect as a prism through which to examine environmental gentrification as a process, I detail life-altering changes and losses that are happening on the ground as Reimagine Galleria and the Green Line proceed as redevelopment initiatives, as well as how these projects are experienced by people who feel both worried about their survival and place in the world and attached to a world that is slipping away as another one takes shape. A queer approach to examining the feeling of environmental gentrification renders visible a range of complex emotional dynamics and more-than-human relations that shape belonging, exclusion, life, and death in gentrifying space. At the centre of this dissertation is an exploration of how feelings are political, of what kinds of communities they bring together, and of what kinds of solidarities they make possible in the face of gentrification.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.027 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".