Re-wilding Parkdale? Environmental gentrification, settler colonialism, and the reconfiguration of nature in 21st century Toronto
Bibliographic record
Abstract
In 21st century Toronto, the labour of caring for urban trees is entangled with both gentrification processes and the social reproduction of settler colonial space. This paper contributes to the study of environmental gentrification through a study of the social reproduction of settler colonial relations to land in the Parkdale–High Park area of Toronto. Specifically, I take up the hyper-visibility of some forms of social reproduction, in order to shed light on how the mundane, quotidian ‘non-work’ of living in/with/for capitalism becomes a site of privilege and a luxury pursuit for more affluent residents. The paper highlights the processes and practices whereby settler colonial urban subjects seek out ‘nature’ as a temporary outside where they can escape from widely accepted downsides of capitalist urbanism, including a diverse array of social and physical ills, from stress, to obesity, to ecological degradation. The paper asks: whose social reproduction does the presence of urban trees serve? In the context of 21st century financialized gentrification, cities are increasingly normalized as spaces of wealth and luxury. It is therefore crucial to pay attention to the raced, gendered, and colonial micro-politics through which urban ecologies are transformed in the service of an anti-democratic vision of the city as a space of leisure and luxury.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".