‘Cleaning up’ the neighbourhood: Affective dynamics of environmental gentrification
Bibliographic record
Abstract
This article examines how affect shapes trajectories of environmental gentrification through a study of greening in several downtown neighbourhoods in Toronto, Canada. Following the 1990s, two decades of greening practices in the West End neighbourhoods of Wallace-Emerson, Bloorcourt, Bloordale, and Sterling Junction have produced geographies of exclusion wherein unruly ‘toxic’ beings have been vilified, policed, and targeted for removal. An affective account of gentrification reveals how certain bodies become marked for intentional displacement. The author explores fear as a powerful affective force, used to justify various environmental and affective practices directed towards ‘cleaning up’ the neighbourhood. These practices serve to disrupt existing more-than-human relations, displacing and marginalizing inhabitants who are understood as ‘invasive’ outsiders and as part of an assemblage of waste that must be removed. Drawing upon queer scholarship and theory, this article delves into the place-specific intertwining of environmental remediation or ‘cleanup’ with social cleansing, arguing that invasiveness, toxicity, and non-belonging are affectively produced.
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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 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".