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Record W2401464652 · doi:10.3141/2546-10

Bigger and Different: Beginning to Understand the Role of High-Speed Rail in Developing China’s Future Supercities

2016· article· en· W2401464652 on OpenAlexaff
Qiyan Wu, Anthony Perl, Jingwei Sun

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsSimon Fraser University
FundersNational Development and Reform Commission
KeywordsChinaEconomic geographyRegional scienceScale (ratio)Transport engineeringTransportation infrastructureTransport infrastructureBusinessEnvironmental planningGeographyEngineeringCartography

Abstract

fetched live from OpenAlex

The development of high-speed rail (HSR) is transforming the landscape of China by enabling new forms of urban and regional expansion. Some of these spatial and economic effects mirror past experience with HSR infrastructure in Japan and Western Europe. But the scale of China’s HSR infrastructure and its role in providing that country’s intercity mobility are sufficiently greater than previous HSR development that new spatial effects can be expected. This paper takes the first step in explaining this transformation by creating an analytical framework to differentiate three modes of spatial development that can be associated with distinct configurations of HSR operation in China. An initial assessment of these transportation development genres is offered, and an agenda for future research into the catalytic role of HSR in China’s development of supercities with more than 130 million people is presented.

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 score0.117
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.005
Scholarly communication0.0030.009
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.081
GPT teacher head0.312
Teacher spread0.230 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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