Fringe stakeholder engagement in protected area tourism planning: inviting immigrants to the sustainability conversation
Bibliographic record
Abstract
Effective and inclusive community participation is an essential and challenging component of sustainable tourism planning and development, especially as communities become increasingly diverse. The establishment of national parks and other protected areas closer to urban areas provides a unique opportunity for investigating community engagement in diverse contexts, as park agencies are mandated to connect with a broader range of community stakeholders. Historically, the engagement of immigrants and minorities with parks and protected areas has focused primarily on visitation, while their role as members of host communities has for the most part been overlooked. This qualitative study, conducted during the development of Canada's first National Urban Park, addresses this need by providing a deeper understanding of immigrants’ engagement in planning. In-depth, semi-structured interviews are conducted with planners, politicians, community organizations, and first-generation immigrants who are now community leaders. The study draws upon, and expands on, earlier work by McCool and by Bramwell. It recommends five underlying principles for more inclusive public conversations: adopting an ongoing, long-term, and communicative approach; being open to new perspectives and willing to revisit assumptions; designing parallel strategies and customized tactics; collaborating with community leaders; and engaging in short-term and long-term learning.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| 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".