International Trade Fairs and Global Buzz, Part I: Ecology of Global Buzz
Bibliographic record
Abstract
This paper investigates the importance of temporary face-to-face (F2F) contact and the physical co-presence of global communities in establishing a particular information and communication ecology during international trade fairs, referred to as “global buzz”. International trade fairs bring together agents from all over the world and create temporary spaces for presentation and interaction. Within a specific institutional setting, participants not only acquire knowledge through F2F communication with other agents, but also obtain information by observing and systematically monitoring other participants. The fact that firms do not necessarily have to be in direct contact with a specific source of information to get access to this knowledge makes participation in these events extremely valuable. International trade fairs have become important expressions of new geographies of circulation through which knowledge is created and exchanged at a distance. This paper analyses the constituting components of global buzz and aims to dismantle the complexity of this phenomenon in a multi-dimensional way. When applying this concept to Internet trade fairs, the question arises whether a similar information and communication ecology, or virtual buzz, can be established. We explore similarities and differences between both forms of buzz, using the same classification scheme.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".