From Crisis to Recovery: Analyzing Government Support for Canadian Restaurants during the COVID-19 Pandemic
Bibliographic record
Abstract
Dr. Julie Kellershohn is an Assistant Professor at the Ted Rogers School of Hospitality and Tourism Management. Her research specializes in consumer decision-making and the impact of marketing and technology in the Foodservice Industry. She focuses on Away-From-Home food and beverage consumption, including Quick Service, Casual, and Full-Service Restaurants. Wayne W. Smith is a Professor at the Ted Rogers School of Hospitality and Tourism Management at Toronto Metropolitan University. His research focuses on tourism policy, consumer psychology and tourism pedagogy. He currently serves on the boards of TTRA Canada and ISTTE. Stephen W. Litvin is a professor of Hospitality & Tourism Management in the School of Business, College of Charleston. Steve received his DBA in International Commerce from the University of South Australia. His primary research interests relate to tourism’s economic and cultural impacts upon communities, tourism consumer behavior and the politics of tourism. Robert E. Frash recently retired after 30 years in food service, and 20 years in academia. He continues his research as Professor Emeritus at the College of Charleston in the USA.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".