Balancing Tourism Development and Sustainability: A Multi-Stakeholder Approach in Tofino over 15 Years
Bibliographic record
Abstract
The narrow conceptualization of capitalism is increasingly challenged as destinations recognize the need to integrate equity and resilience into tourism development. Increasingly, destinations have been using tourism to move away from extractive industries and there is no shortage of literature outlining the need for a more inclusive, multi-stakeholder approach to ensure that sustainability is at the forefront of tourism development considerations. Whether destinations are actually moving towards the more sustainable development of tourism is not always evident and rarely are studies of destinations undertaken over time. Therefore, the purpose of this research was to examine qualitative research spanning 15 years using a multi-stakeholder approach to understand impacts, issues, and solutions relating to tourism development in Tofino, Canada. Tofino has achieved notable advancements in financial, natural, and social capital but continues to face gaps in addressing other dimensions of the Community Capitals Framework (CCF). Insights from this research to focus on social, human, and built capital earlier on in tourism planning and development may help other destinations who partially or fully depend on tourism for their livelihood. This paper addresses real-world tourism development challenges and encompasses multiple stakeholder views on sustainable development, socio-political responsibilities, governance, leadership, destination management, policies, and other practices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".