Blending Amenity Migrants with Locals in Host Destinations: 'Residential Tourism' in British Columbia
Bibliographic record
Abstract
This paper examines how best to capture and manage the benefits of residential tourists and permanent stakeholders in amenity rich host destinations. Building on an emergent model of tourism-led amenity migration management processes, it highlights the perspectives of host destination stakeholders and 'residential tourists' in three separate but complementary case study host regions. It indentifies how each case is responding to the challenges of capturing the benefits of these residential tourists through policy and planning processes. These challenges are explored through a multi-method approach that includes active interviewing, online questionnaires, and community and regional planning policy reviews. Much of the discussion focuses on how the residential tourists are addressing the on-going contestation, negotiation, and eventual transformation of places with their local counterparts. While individual sets of finding emerge from each case, some overall triangulated lessons emerge. In particular, it is a clear that a strategically focused approach, complete with a carefully conceived long term vision for the host community is critical to shaping residential tourism contributions. Such planning should happen early, often and include ways so that all stakeholders can build a vision and direction that reduces vulnerabilities, builds awareness, and captures opportunity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".