Micro firm learning communities in rural tourism: a multi-case study
Bibliographic record
Abstract
Research reveals little about how rural micro firms learn as they collaborate for tourism development. Drawing from Lave and Wenger’s (1991) community of practice perspective, this chapter explores the elements related to micro-firm community learning and presents the findings of an interpretivist multi-case study of two evolving learning communities (ELC) engaged in rural tourism development in Wales and Canada. The literature review reveals catalyst, structure, strategy and boundary as elements which impact sustained learning in this setting. Empirical findings demonstrate that learner autonomy is challenging in both cases while the literary identified elements shaped learning relationships differently in each case. An ELC model adapted from prior research (Reinl and Kelliher, 2014) is presented. Progressive brokerage is emphasised as a fundamental competence for sustainable learning in this environment. Calling for a sustainable learning orientation, recommendations are offered to optimise ELC support and avenues for future research are outlined.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".