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Record W2083253324 · doi:10.1068/a45661

The Development of Trade Fair Ecologies in China: Case Studies from Chengdu and Shanghai

2014· article· en· W2083253324 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

VenueEnvironment and Planning A Economy and Space · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicConferences and Exhibitions Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChinaContext (archaeology)BusinessInternational tradeValue (mathematics)EconomyEconomicsGeography

Abstract

fetched live from OpenAlex

Despite China's rapid economic growth and embedding into global value chains, not much is known about the primary places where buyers and sellers from China and abroad meet, do business, and circulate information and knowledge: That is, the national/international trade fairs in the country. Previous reports suggest that the number and size of such events in China is growing and that the trade fair business is in the process of catching up. Under these circumstances, trade fairs may develop into import or export events, where buyers and sellers engage in transactions, or into temporary clusters, where they exchange knowledge for industrial upgrading and innovation. In this context this paper explores the interaction and communication patterns of firms at Chinese trade fairs and investigates whether these events are similar to those in Europe and North America. The analysis involves systematic comparison of the communication and interaction practices at three national/international trade fairs in Shanghai and Chengdu, based on a total of 102 semistructured interviews.

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 score0.272
Threshold uncertainty score0.336

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 head0.028
GPT teacher head0.258
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