Appreciative Inquiry and Rural Tourism: A Case Study from Canada
Bibliographic record
Abstract
Many Canadian, resource-based communities are facing an economic crisis and often turn to tourism for economic diversification and some recent trends in the growth of tourism employment in Canada's rural areas suggest that such choices are well founded. Despite positive growth indicators, rural tourism is criticized for several reasons, including issues with employment, ownership and lack of understanding of the industry. Although much has been written on the development of community-based tourism and its potential to address such concerns, much of the discussion remains at theoretical levels, with few examinations of practical frameworks for rural communities in crisis, such as the current experience in North-western Ontario, Canada. Enquiries into tourism's contribution to rural community economic development identified two gaps concerning how rural tourism can be a viable industry in resource-dependent communities and how to embed the industry within a community seeking alternatives from a deficit/crisis context. Interviews with a tourism operator in rural Manitoba, Canada seemed to provide an answer to both of these questions, through the application of Appreciative Inquiry (AI) to rural tourism development. Such an examination indicates that although such an approach does not solve the issues, it does provide a new lens through which to understand the potential for tourism in rural communities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.043 | 0.010 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".