MétaCan
Menu
Back to cohort
Record W7142574955

“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

2025· other· en· W7142574955 on OpenAlexaff
Cindy-May Dapaah

Bibliographic record

VenueYorkSpace (York University) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsYork University
Fundersnot available
KeywordsGentrificationRegentRacializationRedevelopmentBoroughSolidarityQualitative research
DOInot available

Abstract

fetched live from OpenAlex

“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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.010
Scholarly communication0.0040.005
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.016
GPT teacher head0.246
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueYorkSpace (York University)French-language works237,207