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Record W4394977203 · doi:10.1177/00420980241235371

Moving through Toronto’s PATH: Assembling private urban governance

2024· article· en· W4394977203 on OpenAlexafffundabout
Debra Mackinnon, Stefan Treffers, Randy K. Lippert

Bibliographic record

VenueUrban Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsUniversity of WindsorYork UniversityLakehead University
FundersSocial Sciences and Humanities Research Council of CanadaGovernment of Canada
KeywordsCorporate governancePath (computing)Economic geographyRegional sciencePublic administrationBusinessPolitical scienceSociologyEconomicsComputer scienceFinance

Abstract

fetched live from OpenAlex

This paper explores Toronto's urban PATH, a 30 km network of underground pedestrian tunnels and elevated walkways that connect shopping areas, residential towers, mass transit and downtown destinations. Both as a case and heuristic, this paper situates Toronto's PATH as an assemblage of private urban governance forms, exploring emergent and evolving constellations of power and responsibility for governing city space that defy easy distinctions of 'public' or 'private'. As an urban assemblage, the PATH comprises potential and actual entities and associations, and is an accumulation of encounters. Never a stable or static entity, the PATH and its governance, we argue, is provisional, revealing constantly evolving connections, alignments and political-economic potentialities. We contend the PATH serves as a palimpsest of mutating governing relations; a multiplicity of meanings, visions and encounters etched into the built environment. By focusing on public and private vestiges, wayfinding, and visibility, and private verticalising ventures, we highlight how practices, logics, processes, urban actors and their histories collide to form fragile, provisional urban alignments and visions.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.841
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.052
GPT teacher head0.346
Teacher spread0.294 · 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 designNot applicable
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

Citations5
Published2024
Admission routes3
Has abstractyes

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