Learning in Context: A Structural Equation Modeling Approach to Analyze Knowledge Acquisition at Trade Fairs
Bibliographic record
Abstract
Abstract Conceptualizations of trade fairs as temporary clusters have identified important learning processes at such events, particularly at leading international trade fairs – both in developed and developing countries. However, little attention has been paid to the home contexts of participating firms that may affect knowledge acquisition patterns. In particular, it is unclear which contextual factors may influence learning behavior. This paper aims to investigate the role of geographical context conditions at the exhibitors’ permanent locations and whether their knowledge acquisition behavior during trade fairs varies systematically with aspects, such as city scale, peripherality, growth dynamics and connectivity. Our analysis is based on a survey of 211 firms conducted between 2014 and 2018 at the China International Industry Fair (CIIF) in Shanghai – one of Asia’s most important manufacturing fairs. Using structural equation modeling (SEM), the study identifies significant pathways of knowledge acquisition and how these differ with geographical context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".