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Record W2170988618

Condominium Development and Gentrifi cation: Th e Relationship Between Policies, Building Activities and Socio-economic Development in Toronto

2009· article· en· W2170988618 on OpenAlexvenueaboutno aff
Ute Lehrer

Bibliographic record

VenueCanadian journal of urban research · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismPraisePovertyProsperityBoomPopulationPoliticsInner cityGeographyPolitical scienceEconomic growthDevelopment economicsSociologyEconomic geographyDemographyEconomicsEngineeringLaw
DOInot available

Abstract

fetched live from OpenAlex

Abstract Over the past few years, Toronto has experienced a massive reinvestment into the inner city, mostly in the form of high-rise condominium towers, which was followed by the largest population growth in over 30 years. Th e city that used to praise itself as multicultural, ethnically diverse and socially-mixed, has, as recent studies indicate, become spatially divided into three distinct cities: the constant city of the rich, the shrinking city of middle-income households, and the grow-ing city of concentrated poverty. In this paper we suggest that the condominium towers are a new form of gentrifi cation that contributes to the spatial trifurcation of the city. We call it the condofi cation of Toronto. We start with a discussion of some aspects on gentrifi cation, followed by an analysis of policy documents and reports that have been guiding urban development in Toronto. We then take a look at the incoming condo-dwellers, before we conclude that the City needs to revisit its planning instruments in order to prevent further spatial segregation in Toronto. Key words: gentrifi cation; condominium boom; Toronto; urban politics and planningIntroduction Th ere has been some discussion lately about the changing face of Toronto’s neigh-bourhoods. Th e report

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.110
GPT teacher head0.381
Teacher spread0.271 · 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 designObservational
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

Citations47
Published2009
Admission routes2
Has abstractyes

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