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Record W2064435305 · doi:10.1068/a46298

Using Toronto to Explore Three Suburban Stereotypes, and Vice Versa

2014· article· en· W2064435305 on OpenAlexaffabout
Richard Harris

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsMcMaster University
Fundersnot available
KeywordsStereotype (UML)Diversity (politics)Ideal (ethics)SociologyCharacter (mathematics)Economic geographyGeographyGender studiesPolitical scienceSocial psychologyLawPsychology

Abstract

fetched live from OpenAlex

Urbanists share and reproduce three stereotypes about North American suburbs. First, many invoke a clichéd ideal: the desire to enjoy quiet privacy in a low-density residential environment near the urban fringe. Second, they assume that most suburbs have actually conformed to this ideal. Third, academics and planners alike agree on a stereotypical judgment: suburbs are to be deplored. This synthetic essay argues that residential patterns in postwar Toronto never conformed to these stereotypes: especially since the 1970s it has harboured a competing, more urbane popular ideal; its suburbs have been socially and physically diverse; and, recognizing diversity, local urbanists have made varied judgments. Suburban diversity has become systematized since the 1970s, so that a new local stereotype has emerged: that of the declining inner suburb. Toronto's experience exemplifies that of one of the two main types of North American metro. It challenges stereotypes, while those stereotypes illuminate its particular character. Most generally, while polycentricity and dispersion have shaped its economic geography, the language of zones is still meaningful in interpreting its residential patterns. There may be a larger lesson there.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.044
GPT teacher head0.256
Teacher spread0.212 · 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

Citations44
Published2014
Admission routes2
Has abstractyes

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