Realizing Rural Community-Based Tourism Development: Prospects for Social Economy Enterprises
Bibliographic record
Abstract
Community-based tourism (CBT) is often considered as one component of a broad-based plan to improve rural economies. CBT development is characterized as a form of locally situated development that uses tourism to generate economic, social, and cultural benefits within a community. This process occurs through increased community participation in decision making and the sustainable development of both natural and cultural resources. Recent work in the field of community economic development has shown that social-economy enterprises, often called the third sector of the economy, can fill multiple areas of need within rural communities, contributing to economic, social, and cultural goals. As opposed to services and businesses controlled by private or public interests, the social economy is made up of community-based and mutually controlled enterprises that exist to serve the identified needs of a specific community. Examples of social-economy enterprises include worker-owned cooperatives, credit unions, community-based training organizations, and volunteer-run projects. This paper examines the potential for social-economy enterprises to contribute to the implementation of CBT within the Canadian rural tourism landscape. Two main roles for the social economy are identified: supportive and product delivery. Each role is described with reference to examples from across Canada. Challenges and benefits within each are evaluated, outlining areas for further research and on-the-ground development of social-economy enterprises to support rural CBT. Keywords: rural tourism, social economy, Canada, social-economy enterprises Many rural
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".