Uncertainty and emotions throughout the Covid-19 pandemic: Festival organizers’ perspectives
Bibliographic record
Abstract
Christine M. Van Winkle, Ph.D. Christine is a professor in Kinesiology and Recreation Management at the University of Manitoba. Christine is committed to community-based research examining visitor experiences at events and attractions. As a former festival coordinator and attraction consultant, Dr. Van Winkle brings both practical experience and theory-based research to inform practice. Driselda Sánchez-Aguirre, PhD. Driselda is a postdoctoral researcher in the Department of Economic Geography at the Institute of Geography, UNAM. Her research interest are tourism, festivals, and cultural heritage. Bingjie Liu-Lastres, Ph.D. Bingjie “Becky” Liu-Lastres, Ph.D., is an assistant professor in the Department of Tourism, Event, and Sport Management at Indiana University. The goal of her research agenda is to promote safe travel and to ensure the health and well-being of tourists, organizations, and employees in tourism and hospitality.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| 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".