Winter tourism experiences and Chinese visitors to the Yukon Territory, Canada: implications for sustainable tourism development
Bibliographic record
Abstract
Winter tourism in the polar and sub-polar regions is flourishing with challenges and opportunities. A culture change involving development of Arctic winter tourism has also been noticed. Yukon Territory is one of the most visited regions in the Canadian Arctic with a relatively fragile environment. Chinese tourists, as an emerging tourism market, have become one of the main target groups of Yukon Territory and in Canada, and understanding the tourism experience is important for future tourism development, which can be achieved by maximally meeting experiential needs and minimizing negative impacts on the sustainable use of tourism resources. The purpose of this research is to understand the experiential features of winter tourism promoted to Chinese visitors and to identify the relationship of experiential features to sustainable tourism development in an Arctic context. A qualitative approach and two methods were used to collect data for content analysis: 1) field notes, using a reflective journal that documented the researcher’s own experience as a participant in the 2018 Yukon Winter Tourism Field School; and, 2) tourism website content analysis, focusing on sixteen Yukon tourism websites selected by the information matrix. Based on the tourism experience conceptual model (Cutler & Carmichael, 2010), an experiential feature matrix was used to allocate relevant information, filter the effective information and analyze visitors’ experience. Then, a sustainable tourism framework, focusing on economic, social-cultural and environmental aspects, was developed through a review of the literature related to sustainable tourism development principles and strategies in the Arctic. Finally, the findings related to the extraordinary experiential features were conceptualized as consisting of three themes that included ‘nature’, ‘unique’ and ‘people-oriented’. The analysis provided insight into how winter tourism experiences aimed at Chinese visitors benefit sustainable tourism beyond economic considerations and how development of winter tourism in the north that extends its benefits to environmental and cultural sustainability, issues that relate to community participation, tourism services and the market segment should be addressed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".