Navigating towards justice and sustainability? syncretic encounters and stakeholder-sourced solutions in Arctic cruise Tourism Governance
Bibliographic record
Abstract
Cruise tourism has a dubious reputation for conspicuous consumption and associated environmental harm.Cruises to the Arctic promise passengers pristine landscapes and authentic and engaging experiences interacting with local and Indigenous communities.Yet, these very environments and communities are under existential threat amidst the climate crisis, provoking the question of how to reconcile the ever-expanding Arctic cruise industry's conflict with the United Nations' Sustainable Development Goals (UN SDGs).To answer this question, the article proposes a novel approach that fuses concepts and methodologies from normative global governance research and critical tourism studies.Based on extensive empirical research in Norway, Canada and Iceland, the article presents stakeholder-sourced solutions that address a variety of justice conundrums associated with the expanding cruise tourism sector in the region.On the basis of the approach developed in the article, our research is able to inform public and policy discourse towards a just and sustainable transition of the polar cruise tourism industry in light of UN SDGs by highlighting the importance of creating 'syncretic encounters' based on four dimensions: authentic storytelling, decompressing spatial and temporal resources, just working conditions, and attention towards the built environment.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.037 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".