Segmenting Sentiment: Categorizing Keen and Averse Travellers during the COVID-19 Pandemic
Bibliographic record
Abstract
Dr. Michael W. Lever is a Lecturer and Post-Doctoral Researcher from the University of Guelph in the School of Hospitality, Food and Tourism Management. His research explores digital marketing and destination brand advocacy. He is a full-time reviewer for the Journal of Travel Research and a TTRA Canada board member. Dr. Michael Mulvey is a marketing professor at the Telfer School of Management and a researcher at the LIFE Research Institute at uOttawa. He is an expert on branding, consumer trends, and marketing strategy. His current research focuses on developing age-friendly business practices in the retail and travel industries. Dr. Statia Elliot is Director of University of Guelph’s School of Hospitality, Food and Tourism Management. She teaches graduate courses, researches destination image, is a Fellow of the Ontario Hostelry Institute, Chair of the Ontario Tourism Education Corporation Board, and board member of TTRA and Women in Tourism and Hospitality. Michel Dubreuil is the Manager of Research, Consumer and Market Intelligence with Destination Canada. He designs and develops economic analysis reports, the destination management framework, and forecasts for the Canadian tourism sector. Michel represents Canada on the United Nations World Tourism Organization Committee on the Statistics and Tourism Satellite Account.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".