Can tourism contribute to social-environmental resilience? Benefits and challenges of tourism mobility in Newfoundland and Labrador
Bibliographic record
Abstract
Throughout the North Atlantic region, nature-oriented tourism is being developed as a strategy for building the resilience of coastal, rural communities. From a tourism mobilities theoretical perspective, successful tourism development means connecting local communities and environments to global flows of people and communication. Much of the tourism mobilities literature focuses on the movements and experiences of tourists. In this paper, by contrast, we focus on tourism impacts within host communities. We draw on field observation and interviews carried out in the Burin Peninsula and Battle Harbour, Newfoundland and Labrador, to examine the benefits and challenges of connecting these local places to large-scale tourism mobility networks. Our results show that in the wake of declining natural resource economies, tourism can help contribute to the social-ecological resilience of coastal communities, with positive social, cultural and economic impacts. There are also, however, challenges inherent to connecting to large-scale tourism mobility networks and the benefits of tourism are not distributed evenly through host communities. We conclude by arguing that conscious efforts should be made to structure tourism development to increase its potential to contribute to the social-ecological resilience of host communities.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".