Bibliographic record
Abstract
Purpose This paper aims to determine a uniquely Canadian training path for tourism companies to follow to embed sustainable tourism practices in their companies. Design/methodology/approach The foundation of this paper was laid by conducting in-depth executive interviews with leading tourism companies in Canada. Based on the interviews, an eight-question survey was developed and sent to 22 Canadian tourism companies with a response rate of 36 per cent. The results of best practice research conducted in the UK and Ireland were considered in relation to implementation in Canada. Findings This paper suggests a Canadian process and key concepts to consider for embedding sustainability in tourism companies. Practical implications This paper provides a practical training process, geared for Canadian tourism companies, that embeds sustainability in all divisions of the company. A step-by-step process is described that all tourism companies, no matter their size, can use to embed sustainability. Originality/value This paper draws upon the author’s experience in working with Canadian tourism companies and incorporates best practices shared in a partnership with The Travel Foundation. As the paper represents both original research and industry best practice, it is of interest to academics, tourism training centres and tourism companies in Canada. Learning an effective and efficient process developed specifically for Canadian tourism companies will allow companies to economically embed sustainability and ultimately create a unique market position for the company.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".