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Reurbanization in Toronto: Condominium boom and social housing revitalization

2010· article· en· W2101334956 on OpenAlexaffabout
Ute Lehrer, Roger Keil, Stefan Kipfer

Bibliographic record

VenuedisP - The Planning Review · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsYork University
Fundersnot available
KeywordsRedevelopmentBoomGentrificationAppropriationPoliticsUrbanizationPublic housingAffordable housingUrban planningPolitical scienceBusinessEconomyEconomic growthEconomic geographyCivil engineeringGeographyEngineeringLawEconomics

Abstract

fetched live from OpenAlex

Over the past few years, a condominium boom has transformed the City of Toronto: developers have bought up land in industrial and residential areas and built high-rise condominium towers that left their imprint on the urban landscape. This new interest for the inner city was sparked by a combination of legal and political shifts that had the intent to redirect growth to already built-up areas, and to change preferences for housing and planning practices that allowed intensification (as well as gentrification) of neighborhoods. In our contribution we will discuss two examples: the condominium boom driven by developers, and the redevelopment of the largest inner city social housing complex in Canada. Both of them, as we argue, are fostered by a market-based approach to re-urbanization. We will pay particular attention to the role that the social construction of a particular urban lifestyle has in the appropriation of spaces for reurbanization.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.329

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.004
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.020
GPT teacher head0.344
Teacher spread0.323 · 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

Citations86
Published2010
Admission routes2
Has abstractyes

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