The cruise tourism industry in the Canadian Arctic: analysis of activities and perceptions of cruise ship operators
Bibliographic record
Abstract
ABSTRACT With the melting of sea ice in the Arctic, the potential for higher shipping access has markedly changed. Shipping activity in the Arctic is increasing, including tourism and exploration activities, underlining the need for reliable communication and monitoring. This article examines the interactions between climate and sea ice change, the patterns of cruise ship tourism through Arctic Canada and the interest of operators to increase their activities in the cruise tourism market in the region. Since 1995, the melting of the summer pack ice in the offers the possibilities of increased shipping in this region while encouraging speculation regarding the potential of the northwest passage (NWP) and the Canadian Arctic to become a major cruise maritime highway. Integrating research from both human and transport geography, this article presents an analysis of vessel movements. It also analyses perceptions of charters and cruise ship operators and of their interests in the cruise tourism market. Discussion is focused on issues associated with the lack of available vessels and maritime infrastructure, regulations in the Canadian arctic waters, security and search and rescue. This research could prove useful for communities, and policy makers, as well as the cruise sector itself, with regard to response to change in these remote locations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".