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Record W4213174065 · doi:10.1080/04353684.2022.2032256

Towers <i>Once</i> in the Park: Uprooting Toronto's Welfare Landscapes

2022· article· en· W4213174065 on OpenAlexafffundabout
Giacomo Valzania

Bibliographic record

VenueGeografiska Annaler Series B Human Geography · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec-Société et CultureMcGill University
KeywordsSociologyContext (archaeology)Urban planningRealmUrban designCompact cityLegitimacyBuilt environmentEnvironmental ethicsLawPolitical scienceCivil engineeringPoliticsGeographyArchaeologyEngineering

Abstract

fetched live from OpenAlex

This article is published as part of the Geografiska Annaler: Series B, Human Geography special issue ‘Revisiting the green geographies of welfare planning’, edited by Johan Pries and Mattias Qviström.ABSTRACT In the midst of the growing ecological crisis, the ‘compact city’ has become the mainstream urban paradigm for the sustainable future of western cities. However, the uneven implementation of densification policies can have adverse impacts on the amount and quality of urban green spaces, which are vital resources for local communities. This paper explores the controversies of introducing compactness in the case of Toronto’s ‘towers in the park’: housing estates built in comprehensively planned neighbourhoods from the 1950s through the 1970s. It does so through the lenses of urban design and landscape planning, by tracking the evolution of narratives that underpin the current urban regime, and by assessing their legitimacy from the perspective of residents. The findings highlight a persistent mismatch between Toronto’s dominant urban design paradigm and the sociomaterial context of its uncritical application. Exemplar episodes of tower infill show two discursive tropes to justify compactness: the alleged underuse of open spaces, and the creation of a proper public realm by replacing these spaces with buildings and streets. Beyond uncovering the fallacy of both claims, this paper outlines an alternative perspective for more equitable strategies for common green spaces, outside unconditional protection and zealous quest for “value uplift.”

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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.

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

Citations7
Published2022
Admission routes3
Has abstractyes

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