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Record W1548958516 · doi:10.24908/jcri.v2i1.4295

The Re-Branding Project: The Genealogy of Creating a Neoliberal Jane and Finch

2012· article· en· W1548958516 on OpenAlexaffvenueabout
Suzanne Narain

Bibliographic record

VenueJournal of Critical Race Inquiry · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsFinchGentrificationNeighbourhood (mathematics)PraxisSociologyPoliticsIdeologyContext (archaeology)Gender studiesPublic relationsPolitical scienceMedia studiesGeographyEconomic growth

Abstract

fetched live from OpenAlex

This research examines the stigmas that have been given to the Jane and Finch area in Toronto, Ontario and the effects of re-branding the neighbourhood as “University Heights”. The re-branding initiative started in 2006 and has rapidly changed the face of the Jane and Finch community with the development of new housing complexes and a subway expansion. Using a genealogical approach I trace the steps that were taken to develop “University Heights” to determine if a democratic process was used throughout the decision-making phases. I outline the key social, political and economic stakeholders that played a role in the re-branding project. In the context of neoliberal praxis, I use the public statements made by the stakeholders to unpack what the re-branding initiative entails and highlight whose interests it is likely to serve. This research calls attention to the ways in which residents of the Jane and Finch area will be affected by the gentrification of their neighbourhood. A critical race framework is used to uncover the neoliberal ideologies that have been fundamental to the creation of “University Heights”. The crux of my project is to highlight the social injustices along the axis of race, class and gender, that are embedded in applying a neoliberal agenda in the Jane and Finch area.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.073
GPT teacher head0.405
Teacher spread0.332 · 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 teacher head, 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

Citations17
Published2012
Admission routes3
Has abstractyes

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