Innovations in hotel administration in Canada
Bibliographic record
Abstract
Purpose This paper aims to analyse challenges, trends and innovations in the hotel industry in Canada, focusing on large corporate hotels as well as small limited service hotels. Design/methodology/approach The foundation for this paper was laid during a well attended Worldwide Hospitality and Tourism Themes (WHATT) roundtable discussion between industry leaders and hospitality educators in May 2012. The subject of hotel administration was discussed in the context of the theme for the 2012 Canadian WHATT roundtable and the strategic question: “What innovations are needed in the Canadian hotel industry and how might they be implemented to secure the industry's future?” Findings The paper presents findings from a recent survey on strategic issues compiled by hotel managers in the greater Toronto area (GTA). The paper lists valuable information on innovative practices in different types of hotels. Practical implications Practical tips in the body of the paper and in the conclusion section are provided. Originality/value As the team of authors includes a former president of a Canadian hotel company, a former international hotelier, and the current general manager of the largest hotel in the capital city of Canada (Ottawa), this paper will be of immense value to students, educators, and researchers, as well as industry leaders. The paper draws on expert experiences to explain how innovative initiatives can be implemented in order to achieve greater success in hotel administration.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".