Perspectives of Decision Makers and Regulators on Climate Change and Adaptation in Expedition Cruise Ship Tourism in Nunavut
Bibliographic record
Abstract
Increases in Arctic tourism over the past few decades have occurred within a context of change, including climate change. This article examines the ways in which tourism decision makers and regulators in Nunavut view the interactions of cruise ship tourism and climate change, the challenges presented by those interactions, and the opportunities available within this context of change. The article uses an approach that is aimed at assessing sensitivities and adaptive capacity in order to develop strategies for managing change. It describes the findings from thirty-one semi-structured interviews conducted with federal government, Government of Nunavut, and industry personnel and managers involved in Nunavut’s tourism industry. The two major themes of the article are the growth and adjustment in the cruise tourism industry stemming from climate change and the governance issues that are associated with these changes. A strong focus in the interviews was recognition of the need for a collaborative approach to managing the industry and the need to enhance and extend territorial legislation to ensure a safe and coordinated industry to provide benefit to Nunavut, the communities that host the ships, the industry, and the tourists. The article concludes that decision makers and regulators need to address the compounding of challenges arising from tourism and climate change through a multi-level stakeholder approach.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".