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Record W4206464345 · doi:10.5604/01.3001.0014.8795

Understanding the urbanization impacts of high-speed rail in China

2021· article· en· W4206464345 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

VenueArchives of Transport · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsUrbanizationChinaTransport engineeringEconomic geographyRail networkPopulationGeographyEngineeringBusinessRegional scienceCivil engineeringEconomic growthEconomics

Abstract

fetched live from OpenAlex

Advances in transport technology have been shown to play a vital role in urban development over millennia. From the engineering and pavement innovations of the Roman road network to the aerospace breakthroughs that enabled jet aircraft, cities have been reshaped by the mobility changes resulting from new designs for moving people and goods. This article explores the urbanization impacts of High-Speed Rail’s introduction in China, which has built the world’s largest High-Speed Rail network in record time. Since High-Speed Rail was launched in Japan in 1964, this technology has worked to reshape intercity travel as a revolutionary transportation alternative. High-Speed Rail has developed steadily across Japan, France, Germany, Italy, Switzerland during the 1970s and 1980s. It expanded to Russia, Spain, the United Kingdom, and Sweden in the 1990s. In the 21st century, China began developing High-Speed Rail on an unprecedented scale, and now has a national network that is longer than the totality of the rest of the world’s High-Speed Rail operations combined. China’s High-Speed Rail operation is exerting a transformative influence on urban form and function. This article synthesizes secondary research results to analyse the impacts of HSR on urbanization. These effects include population redistribution, urban spatial expansion and industrial development. We offer a typol-ogy that considers the urban effects of High-Speed Rail at three spatial levels: the station area, the urban jurisdiction, and the regional agglomeration. When organized through our typology, research findings demonstrate that High-Speed Rail influences urban population size, urban spatial layout and industrial development by changing the acces-sibility of cities. We highlight the processes by which High-Speed Rail ultimately affects the urbanization process for people, land use, and industrial development. However, High-Speed Rail’s impacts on urbanization are not always positive. While leveraging the development opportunity enabled by High-Speed Rail, governments around the world should also avoid potential negative impacts by drawing lessons from the experience of High-Speed Rail’s rapid de-ployment in China.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.051
GPT teacher head0.219
Teacher spread0.168 · 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