An Exploration of Evolving Learning Communities in the Micro Firm Rural Tourism Context: A Multi-Country Study
Bibliographic record
Abstract
Rural stakeholder collaborations are considered pivotal to successful rural development. In this context a growing body of micro firm related tourism research acknowledges the value of collaborative learning networks and the learning relationships that develop within. However little research reveals how micro firms learn independently in the practice of tourism development in an ‘evolving learning community’ context. Drawing from Lave and Wenger’s (1991) community of practice perspective, this research seeks to explore the elements and relationships that influence learning in an evolving learning community (LC) in the micro firm rural tourism context. An evolving LC is defined as a group of businesses (micro firms) who collaborate with one another and other stakeholders in their community for the purpose of tourism development; in doing so they build shared meaning and learn in practice, as the community evolves from one stage to another. A comprehensive literature review reveals key criteria which influence evolving LC structures and interrelationships. These criteria are explored through two longitudinal interpretive case studies in tourism practitioner communities in Canada and Wales. Employed research techniques comprised interviews, observation, LC communication review and reflective diary maintenance. The findings offer insights into how the catalyst, structure and leadership, learning strategies, LC resources, communication, participation and identity and boundary criteria support or impede micro firm learner autonomy and influence the evolving LC’s learning dynamic. Recommendations are offered into optimised evolving LC support mechanisms at local, regional and national level; ultimately contributing to rural regional policy development in each domain.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".