Increasing sustainable tourism through social entrepreneurship
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the link between social entrepreneurship and sustainable tourism and to examine the Canadian context in this regard. Design/methodology/approach The methodology entails a case study approach that includes a thorough review of the related literature and of any existing Canadian sources of hospitality and tourism social entrepreneurship/intrapreneurship projects to determine the state of the Canadian industry with respect to sustainability. Findings Findings show that there are limited showcased hospitality and tourism social entrepreneurship projects in Canada. Two main assumptions related to the Canadian context can be drawn from this search: (1) There is a lack of hospitality and tourism social entrepreneurship projects and/or, (2) hospitality and tourism social entrepreneurship projects and/or businesses are not recognized and/or there is a lack of awareness of them. Research limitations/implications This study assessed the situation in Canada and although it was comprehensive under conditions of limited data availability, it cannot speak to social entrepreneurship in sustainable hospitality and tourism globally, which is a future research opportunity. Practical implications The design of a national incentive program would encourage industry sustainability through tax breaks. This voluntary system would require that firms provide standardized annual reports with their tax filings so that reliable industry data could be collected for analysis and understanding of the sustainability of the industry. Participating firms would be distinguished on a public list. Originality/value This research has theorized on the connection of social entrepreneurship to sustainable hospitality and tourism such that social entrepreneurship drives sustainable industry growth. This is also the first study of its kind to explore social entrepreneurship’s potential contribution to the sustainability of this industry.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".