WHATT roundtable: what innovations would enable tourism and hospitality industry to re-build?
Bibliographic record
Abstract
Purpose The aims of this paper is to present views of 13 experts who attended a roundtable discussion. Design/methodology/approach This article provides a narration of a conference roundtable. The questions by the moderator and a summary of the responses by panellists are provided. Findings Worldwide Hospitality and Tourism Themes (WHATT) is playing a significant applied research role in the world of hospitality and tourism. Further, since 2013, the International Conference on Hospitality and Tourism Management (ICOHT), had been well attended and successful. In 2021, The International Institute of Knowledge Management included a WHATT roundtable in the programme of the 8th ICOHT. Twelve experts from the industry and academia were invited as panellists. They represented eight countries (Canada, Guyana, Iran, Jamaica, the Philippines, New Zealand, Sri Lanka and the USA). The lead author of this article moderated the roundtable discussion. In conclusion, 20 of the key implementable concepts and suggestions for the post-pandemic era within hospitality and tourism industry, evolved from the WHATT roundtable at the 8th ICOHT in 2021, are presented. Originality/value This paper suggests 20 of the key implementable concepts, for post-pandemic era tourism and hospitality industry.
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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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".