Tales from the frontier of sustainable global connectivity: A typology of Arctic tourism workers
Bibliographic record
Abstract
The Arctic is both known for its picturesque and threatened environment, warming at four times the global average. As tourists continue to flock to the region to witness its natural beauty and decline, they create ‘connectivities’ between the global and the local, which raise the question of whether tourism can play a role in sustainable global relations. This article advances interdisciplinary research that approaches ‘the global’ as a local phenomenon. It does so by broadening the category of ‘tourism workers’ to include hospitality providers, local municipalities, and tour operators in addition to tour guides, and by operationalising Arendt’s practice of ‘visiting’ and Curtin and Bird’s typology of Aboriginal tourism guides. Drawing on data that was co-produced in collaboration with tourism workers in three Arctic states (Canada, Iceland, Norway) via 50 qualitative interviews, participant observation, and a workshop, the article outlines three types of Arctic tourism workers: the Indigenous/Local Storytellers, the Sustainability Educators, and the Safety Experts. Identifying these types, and the motivations and tourist interactions they are associated with provides insights into tourism education and policymaking that can enhance interactions between different global regions and make global ‘connectivities’ more sustainable.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".