From Toxic Wreck to Crunchy Chic: Environmental Gentrification through the Body
Bibliographic record
Abstract
This paper takes up the challenge of extending and enhancing the literature on environmental gentrification by considering bodies and embodied practices as significant dimensions of this process. In considering the question of how a polluted past can be mobilized as an asset for neighbourhood rebranding and gentrification, this research suggests that the conflation of both pollution and ‘health’ with different kinds of urban bodies and practices is an important strategy for solidifying a clean and green neighbourhood future. I argue that some bodies are constituted as ‘dirty’ by the symbolic and substantive displacement of environmental pollution onto those bodies, in ways that allow the neighbourhood to redefine itself as clean (whether it is environmentally clean or not) once those bodies are displaced, contained, or made invisible. This perspective requires us to consider the radically coconstitutive character of representations and materiality, bodies and cities, nature and social relations. Based on a case study of Toronto's Junction neighbourhood, this paper maintains that bringing bodies to the foreground attends to the power of embodiment in producing and reproducing urban change and, critically, urban inequalities.
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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 | 0.002 | 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 | 0.007 | 0.061 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 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".