“And so, my heart was constantly just wanting to be home, but nowhere was home”: A Study into the Voices of Displaced Regent Park Residents
Bibliographic record
Abstract
“A united racialized community to exist on its own terms and to be subject to the same rights and freedoms as the greater white community, remains inconceivable” (Nelson, 2002, p. 129). The gentrification of Regent Park illustrates this reality. This study asks: How is gentrification used as a neoliberal tool to dismantle racialized communities? This research draws on Critical Race Theory (CRT), post-structuralism and spatial theories to examine how policies like “social mix” mask displacement as revitalization. This paper will analyze how redevelopment policies have perpetuated systemic inequities while disrupting vital networks of solidarity and care. Through stories of displaced community members from Phase 2 of the revitalization project, this qualitative study highlights the cultural wealth, resilience, and deep-rooted sense of belonging in Regent Park before gentrification. The findings challenge the logic of social mix and neoliberal assumptions that low-income communities cannot thrive without proximity to whiteness or middle-class norms. Ultimately, this study argues that the erasure of community was not an accidental outcome but a systemic effect of redevelopment efforts that prioritize market interests over the lives of racialized residents.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".