The Development of Trade Fair Ecologies in China: Case Studies from Chengdu and Shanghai
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".