BRIDGING THE TOURISM PLANNING GAP: CREATING A REGIONAL RURAL TOURISM PLANNING ALLIANCE FOR COMMUNITIES-IN- TRANSITION ON VANCOUVER ISLAND
Bibliographic record
Abstract
In recent decades, rural Vancouver Island has witnessed dramatic decline in its traditionally dominant economic sectors of forestry and fisheries and major growth in its service industry sectors, especially tourism (Robinson and Mazzoni, 2004; Vaugeois, 2003). However, while a local economy driven by tourism appears as an attractive escape from the boom and bust cycle of resource dependency, the particularities of rural communities-in-transition (RCIT) and the volatile and often ecologically and culturally damaging nature of the tourism industry itself present challenges in the quest for a viable, community driven tourism product (Robinson & Twynam, 1997; Swarbrooke, 2000). From an industry perspective, the potential for rural tourism development on Vancouver Island- and across Canada- has not yet been fully realized. At the same time, however, many rural areas are under-serviced, lack adequate planning systems or the capacities needed to embark on the development of a new and often misunderstood industry. Local planning, and tourism planning support from applied research teams, take on a new importance when small communities are confronted with changes that are beyond local capacities to significantly influence. If Canada wishes to maintain 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 | 0.002 | 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 | 0.016 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".