The Influence of Educational Dimensions of Arctic Expedition Cruising on Post-Cruise Environmental Attitude
Bibliographic record
Abstract
Cruising is a segment of tourism that is increasing at a faster rate than other formats of leisure travel (CLIA, 2014; Luck, 2007), especially in the Arctic region (Luck, Maher & Stewart, 2010). Due to milder weather conditions in recent years, ships have been able to access more regions during a longer operating season. In addition, increasing participation in “last chance tourism” is confirmed to cause a further increase in visitors (Eijgelaar, Thaper, & Peeters, 2010). \tThe educational impact of expedition cruising on cruisers has been researched in Antarctica (Powell, Kellert & Ham, 2008) and Australia (Walker & Moscardo, 2006). However, in the 30 years that expedition cruising has occurred in Canada’s Arctic, little research has focused on the immediate influence of these immersive tourism experiences on cruisers (Green, 2010). Arctic cruise lines have developed a range of educational programs that address the presumed need of cruisers for an educational experience. Pre-embarkation packages might include a variety of resources from company-specific handbooks to suggested reading lists. Field staff may present specialized lectures on destination-specific topics during time at sea, as well as lead excursions on shore (Douglas & Douglas, 2004). \tThis study explores the educational dimensions of expedition cruising in five phases and determines the relationship between expectations, program delivery, and engagement. Motivations and expectations of cruisers were identified using entrance surveys prior to embarking on the expedition. Motivations are found to be important to cruisers when determining a cruise package to purchase. \tThe study investigates key motivations for Arctic cruising, and in forms on levels of escape, socialization, learning, sightseeing and adventure. The study also determines the level of importance educational programming has on cruiser expectations. A qualitative approach included interview and observation on the vessel to support survey findings. These assessed the level of education and experience that each lecture guide had in his/her area of specialization, as well as the lecture content and delivery. Analysis included a manifest content analysis of the questions posed during lecture sessions and excursions, measured against an adapted Bloom’s Taxonomy scale. The unique backdrop of the Arctic and a mixed method research approach can add to cruising literature on environmental education reform. This research provides valuable insight into the educational motivations of expedition cruisers. Learning opportunities are an important component of the cruise experience, which has potential to positively impact cruiser attitude 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".