How should Canadian tourism embrace the disruption caused by the sharing economy?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it