How should Canadian tourism embrace the disruption caused by the sharing economy?
Bibliographic record
Abstract
Purpose This paper aims to answer two questions: What is the sharing economy? and How is the sharing economy affecting tourism in Canada? Design/methodology/approach The foundation of this paper was laid during a major industry event held in Ottawa in 2016 – the Ontario Tourism Summit, an annual industry conference organized by the Tourism Industry Association of Ontario (TIAO), attended by 650 industry participants. This paper is based on presentations made at the summit. The article provides key information on Airbnb and the role of TIAO in the context of shared economy. Findings Companies such as Airbnb, Uber and Turo have made the concept of sharing economies an everyday concept. As sharing economy is considered as a phenomenon that is here to stay, Canadian tourism and hospitality industries should embrace the disruption caused by it and ensure that this is done for mutual benefit of all stakeholders. Five key suggestions are made by the authors in their conclusions. Practical implications As this paper is mainly based on the authors’ viewpoints, prior to implementing their recommendations, further dialogue with all relevant stakeholders is needed. Originality/value This paper draws upon the authors’ experience working with Canadian tourism companies and incorporates their thoughts for practical solutions.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.023 | 0.021 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".