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Research on Global Exhibition Industry Management Models and Their Implications for China's Regional Exhibition Industry

2024· article· en· W4395073613 on OpenAlexaboutno aff
Zhigang Dun, Fanjing Liu

Bibliographic record

VenueInternational Journal of Management Science Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicConferences and Exhibitions Management
Canadian institutionsnot available
Fundersnot available
KeywordsExhibitionChinaBusinessPolitical scienceVisual artsArt

Abstract

fetched live from OpenAlex

The exhibition industry, as an integral part of the modern service sector, functions as a catalyst for regional economic development and is one of the key indicators of a region's openness and developmental potential. With over 170 years of growth, international exhibition industry management models have matured significantly. Developed countries such as Germany, the United Kingdom, France, and Canada have gradually established four predominant development models: government-led, market-led, association-led, and a hybrid of government and market. These models hold a dominant position in the global development of the exhibition industry. The pace and level of development of the exhibition industry largely depend on the chosen management model. An appropriate industry management model can foster rapid development of the exhibition industry, whereas an unsuitable one can hinder progress. Under the new circumstances, China insists on implementing more extensive, broader, and deeper opening-up policies, accelerating the internationalization of the exhibition industry and gradually forming five major regional clusters: the Yangtze River Delta, the Pearl River Delta, the Bohai Rim, the Northeast, and the Midwest. In this context, analyzing international exhibition industry management models is of significant importance for improving China's exhibition industry management and promoting high-quality development of regional exhibition industries 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.

How this classification was reachedexpand

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)0.014
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.728
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0020.001
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 head0.244
GPT teacher head0.507
Teacher spread0.263 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations0
Published2024
Admission routes1
Has abstractyes

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