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Record W4387306159 · doi:10.1080/17549175.2023.2262698

Urban development & street-network sprawl in Tokyo

2023· article· en· W4387306159 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Urbanism International Research on Placemaking and Urban Sustainability · 2023
Typearticle
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsUrban sprawlMetropolitan areaGeographyContext (archaeology)Urban planningEconomic geographyRegional scienceUrban networkStreet networkEnvironmental planningCartographyTransport engineeringCivil engineeringArchaeologyEngineering

Abstract

fetched live from OpenAlex

This paper uses Tokyo, Japan as a case study to explore the processes of urban development and sprawl through the lenses of street connectivity and historical development. Tokyo is a highly connected city in terms of its street network, but also offers a unique urban experience of global renown. This study develops a novel time series that shows the evolution of patterns of connectivity across the urban region. We then qualitatively analyze this time series in the context of Tokyo’s urban history to identify the historical and present-day influences that have shaped its street connectivity. We find that streets in the Tokyo metropolitan region have always been remarkably connected by international standards, and that this connectivity has persisted throughout different periods of economic and governmental systems in addition to both natural and anthropogenic disasters.

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.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.055
GPT teacher head0.352
Teacher spread0.297 · 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