Expedition Cruising in the Canadian Arctic: Visitor Motives and the Influence of Education Programming on Knowledge, Attitudes, and Behaviours
Bibliographic record
Abstract
Cruising is a segment of tourism that is increasing at a faster rate than other kinds of leisure travel, especially in the Arctic region. Due to changing environmental conditions in recent years, cruise ships have been able to access more regions of the Arctic for a longer operating season. We investigated the cruiser motivations for polar expedition cruising and the educational dimensions of expedition cruising. Motivations of cruisers were identified using entrance surveys prior to embarking on four separate itineraries (n = 144). We conducted semi-structured interviews, n = 22), made participant observations while on board the vessel for one trip to support survey findings, and followed up with a post-trip survey to assess attitudinal changes (n = 92). We found that, unlike mainstream cruisers, expedition cruisers are motivated by opportunities for novel experience and for learning. Subsequently, the educational programming offered by expedition cruise companies is an important component of the cruise experience. We found that this programming has positively impacted cruiser attitudes, behaviours, and knowledge post-cruise. These findings will encourage cruise companies to improve their educational offerings (i.e., preparedness, program quality, level of engagement) to meet the expectations of their clientele, thereby transferring critical knowledge of environmental stewardship.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".