Bibliographic record
Abstract
For many knowledge workers, the rhythm of organizational life is punctuated by attending temporary gatherings such as conferences, trade shows, hackathons, or festivals, where they mingle with counterparts from other organizations. Some temporary gatherings become wellsprings of invention, fostering the remixing of diverse prior knowledge. Other temporary gatherings become echo chambers, reinforcing the status quo. Despite their ubiquity, surprisingly little research addresses how and why some temporary gatherings foster new ideas while others do not. In this paper, we put forth a conceptual model of knowledge recombination at temporary gatherings. Building on research on the complementary cognitive, structural, and relational antecedents of innovation, we identify how knowledge recombination at temporary gatherings differs compared with formal organizations and how highly generative gatherings differ from less generative gatherings. Many gatherings fail to realize their knowledge generation potential because common networking behaviors create “temporary silos” of segregated knowledge. In contrast, generative gatherings remix diverse knowledge through inherent randomness, emergent small-world search, and boundary-spanning gathering designs. We contribute to innovation research by conceptualizing temporary gatherings as a distinctive social context for knowledge creation and by articulating how gathering design can become a subfield of organization design.
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.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".