The Canadian hotel industry: innovative solutions to secure the industry's future
Bibliographic record
Abstract
Purpose This paper aims to present practical answers to the strategic question: “What innovations are needed in the Canadian hotel industry and how might they be implemented to secure the industry's future?” It aims to capture the essence of conclusions of seven papers written by 23 experts on aspects related to the hotel industry of Canada for the Worldwide Hospitality and Tourism Themes (WHATT) issue on Canada in 2013. Design/methodology/approach The approach of this paper is to integrate all solutions suggested in these seven papers and to seek a succinct response to the strategic question. Findings While providing a helicopter view of the key trends and challenges of the hotel industry of Canada, this paper proposes implementable and practical solutions to those challenges. Using the 2012 WHATT Roundtable discussion in Ottawa, Canada as the foundation, this paper addresses some of the most significant issues affecting the hotel industry of Canada today. In conclusion, 12 key suggestions are made. Practical implications The paper reviews past concepts and industry practices as well as current practices to identify practical, effective and innovative approaches for the future. Originality/value This paper provides fresh perspectives on many relevant issues by analysing inputs, viewpoints, comments, and suggestions of many subject experts. Readers with interests in the hotel industry in Canada or similar tourism destinations around the world would benefit from reading this paper.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".