Bibliographic record
Abstract
Abstract This paper builds on discussions about field-configuring events (FCEs) and cyclical/temporary clusters by investigating the role of trade fairs in structuring processes of knowledge creation in an industry or technology field. It argues that while fundamental field-configuring activities, such as shifts in technological trajectories, are not typically associated with trade fairs, these events play an important role in field reproduction through decentralized processes of knowledge exchange and learning, supported by the global cycles of events. Yet, knowledge flows across different events are rarely as continuous and fluid as in an ideal-type cyclical cluster context. Despite some overlap in their goals and audiences, different trade fairs generally serve different functions and are characterized by diverse knowledge practices. This is illustrated through an empirical analysis of the global trade fair cycle of the lighting industry, which is based on semi-structured interviews and systematic observations conducted at three international/national trade fairs: LightFair International (USA), IIDEX/NeoCon Canada and Light + Building (Germany). From this, we suggest that most trade fairs establish a permanent middle-ground between, but quite distant from, the extreme ideal-types of discrete field-configuration and continuous knowledge circulation.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".