Identifying and evaluating adaptation strategies for cruise tourism in Arctic Canada
Bibliographic record
Abstract
The cruise tourism industry in Arctic Canada has recently grown rapidly with stable numbers now emerging. While there are many socio-economic opportunities associated with growth, climate change, and environmental, technical and cultural risks also present significant management challenges. To enhance understanding of these opportunities and risks, this study adopted a policy Delphi approach to identify and evaluate potential adaptation strategies to aid decision-makers and policy-makers managing cruise tourism development and its associated impacts. Over 500 ideas were identified. These were distilled down to 65 potential adaptation options, which were evaluated for priority and feasibility by key stakeholders including local residents, tourism operators, and policy-makers. The majority of recommendations were evaluated as of high priority and most options were perceived to be somewhat affordable and implementable. Key needs included disaster management plans, updated technology and ship navigation systems, improved marine resource mapping, and the development of a code of conduct for cruise tourists to guide visitor behaviour and promote a sustainable approach. The research represents the first empirical study to identify and evaluate adaptation strategies for cruise tourism development in Arctic Canada and outlines current priorities, opportunities, and challenges associated with managing socio-economic change in Arctic Canada in sustainable ways.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".