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Record W2949597760 · doi:10.1080/02723638.2019.1630209

Paving the way to growth: transit-oriented development as a financing instrument for Shanghai’s post-suburbanization

2019· article· en· W2949597760 on OpenAlexfundno aff
Jie Shen, Fulong Wu

Bibliographic record

VenueUrban Geography · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
FundersEconomic and Social Research CouncilUrban Studies FoundationSocial Sciences and Humanities Research Council of CanadaFudan UniversityNational Natural Science Foundation of ChinaUniversity College London
KeywordsSuburbanizationUrban sprawlLeverage (statistics)Transit-oriented developmentBusinessChinaFinancePublic transportLand useReal estate developmentLand developmentTransport engineeringEconomic growthReal estateGeographyEconomicsEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Chinese suburbs are dominated by extensive high-density projects around transit-oriented development (TOD), but the form of development and the role of rail transit have not been fully investigated. Based on a case study of the No. 9 metro line in Songjiang, Shanghai, this paper examines the logic and implementation of TOD in China. We argue that rather than an effective tool for curbing suburban sprawl, TOD is appropriate to facilitate suburban growth by linking land sales, property development, and infrastructure funding. Rail transit, therefore, functions as an instrument for financial leverage. Despite great economic success, however, the provision of public facilities and services for the large number of residents has raised new challenges for the local government.

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)0.001
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 score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.236
Teacher spread0.227 · 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

Citations56
Published2019
Admission routes1
Has abstractyes

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