Making Good: Racial Neoliberalism and Activist Subjects in Toronto's Parkdale Neighbourhood
Bibliographic record
Abstract
This dissertation examines the complex roles the Parkdale Activity-Recreation Centre (PARC), a progressive social service agency, has played in Toronto’s gentrifying Parkdale neighbourhood. Emerging from the author’s experiences as a PARC worker, this research juxtaposes the agency’s rhetorical and material investments in opposing gentrification and neoliberalism with its ongoing momentum towards privatization, and spatial and social enclosure. It suggests that the key to understanding these contradictions lies in the construction of the enlightened bourgeois activist, a subject whose genuine desire for personal and political “goodness” both reinforces and obscures racial and gendered violence. Relying on an existing textual archive, ethnographic observation, and extensive interview data, this dissertation tracks the racial and gendered strategies of gentrification and neoliberalism through descending scales: the Parkdale neighbourhood; the institutional and spatial environments of PARC; and the interpersonal and intrapsychical relationships between and among PARC staff. At every level, the enlightened bourgeois activist emerges as both an architect and an effect of existing power structures. Ultimately, this dissertation argues that so long as we leave race and gender uninterrogated in our external and internal lives, the social work that we imagine to be emancipatory will reinforce those systems of domination we hope to oppose.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.047 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 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".