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Record W2046024973 · doi:10.1080/02723638.2013.778651

Metropolitics in Motion: The Dynamics of Transportation and State Reterritorialization in the Chicago and Toronto City-Regions

2013· article· en· W2046024973 on OpenAlexaffabout
Jean‐Paul D. Addie

Bibliographic record

VenueUrban Geography · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsYork University
Fundersnot available
KeywordsEconomic geographyDynamics (music)State (computer science)Motion (physics)GeographyRegional scienceSociologyEconomic growthEconomicsComputer scienceClassical mechanicsPhysics

Abstract

fetched live from OpenAlex

The global economic crisis exposed the instability of financialized urban governance at precisely the moment when governing coalitions have launched ambitious, expensive plans to reimagine urban transportation infrastructure, driven by the imperatives of restoring accumulation amid intensifying economic and regional competition. In Chicago and Toronto, processes of urban restructuring and state reterritorialization disclose contradictory tendencies in the city-regions’ modes of urbanization. Tracing the contingent path-dependencies of transportation crises highlights tensions between, and within, preexisting metropolitan dynamics and an ascendant neoliberal city-regionalism. The mobilization of collective regional agency appears necessary to overcome the inertia of divisive metropolitan politics, yet the specific political–economic contexts of the case city-regions significantly condition the structural capacity of actors producing, and the potential articulation of, emergent city-regional governance.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.251
Threshold uncertainty score0.505

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.009
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.236
Teacher spread0.228 · 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 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

Citations35
Published2013
Admission routes2
Has abstractyes

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