Brokering sustainable learning communities: a rural micro-firm capability framework
Bibliographic record
Abstract
Purpose -This poster exhibits the literary-identified capabilities required by micro-firm tourism practitioners to 'broker' local tourism practice in interaction with the broader tourism stakeholder base.Design/methodology/approach -Adopting a learning community focus, guided by the understanding that individual learning and capability development occurs within a social context (Lave & Wenger, 1991) the authors analyse relevant tourism network and dynamic capabilities literature and catalogue micro-firm broker capability criteria based on the findings.Background -In Europe, micro-firms employ less than ten (European Commission, 2011), while Industry Canada defines a micro-firm as one with fewer than five employees (Industry Canada, 2013).The overwhelming majority of tourism firms are micro in size and are instrumental in the economic growth, competitiveness and employment of rural communities (Johnson, Sear & Jenkins, 2000;Saxena, Clark, Oliver & Ilberry, 2007).These firms employ in excess of 7.7million people in Canada, and when combined with small firms they account for 98.2% of all Canadian businesses (IC, 2013).Canadian micro-firms' main focus is concentrated on the local market, with 73% of the firms having over 60% of their market concentrated in the local community (IC, 2002).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".