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Record W2909043240 · doi:10.1080/24704067.2018.1531246

Stadium Games in Entrepreneurial Cities in China: A State Project

2019· article· en· W2909043240 on OpenAlexaff
Hanhan Xue, Daniel S. Mason

Bibliographic record

VenueJournal of Global Sport Management · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsStadiumChinaState (computer science)AdvertisingArchitectural engineeringCivil engineeringPolitical scienceGeographyBusinessEngineeringComputer scienceMathematicsArchaeologyGeometry

Abstract

fetched live from OpenAlex

Cities in China have started to build sport-related infrastructure in earnest; over 1.6 million sport facilities (including 1,093 large-scale sport stadiums and arenas) were built in China from 2003 to 2013. However, operating losses have created concerns of a debt crisis, sparking debate as to whether stadium and arena development can improve the quality of life of residents. This paper examines China’s arena development strategy over the past decade, and finds that development has been undertaken by local governments for two purposes: (1) local development and growth to increase urban competitiveness; and (2) establishing “human-centered” infrastructure to symbolically position the city to gain the state’s sustained support in competition for resources with other cities. Thus, cities do not invest public funds principally in the name of increased tourism, commercial development, or even civic pride but rather as a means for the city to be elevated in the eyes of the central 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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.308

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.002
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 head0.293
Teacher spread0.284 · 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 designQualitative
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

Citations19
Published2019
Admission routes1
Has abstractyes

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