Wild bears, real bears and zoo bears: Authenticity and nature in Anthropocene tourism
Bibliographic record
Abstract
Within nature-based tourism research, authenticity has received a great deal of attention in relation to existential authenticity and in examining the authenticity of experiences. Yet very little research exists that explores the ways in which tourists perceive wildlife as more or less authentic, as objects in nature-based tourism discourses. This qualitative case study research explores visitors' perspectives in relation to polar bear tourism in Churchill, Manitoba (in situ) and at the Assiniboine Park Zoo's 'Journey to Churchill' exhibit (ex situ) in Winnipeg, Manitoba. The 'Journey to Churchill' exhibit was built with the intention of representing aspects of the landscape, wildlife and town-site found in and around Churchill, Manitoba. These two sites provide a unique opportunity to compare in situ and ex situ nature-based tourism experiences, since the sites have similar elements such as wildlife species, landscape features and other contextual factors (such as environmental issues and cultural influence). The findings from this research suggests that perceived authenticity of the polar bears, more than the experience, contributes to the construction of learning experiences about climate change. We review the work of authenticity in nature-based tourism and suggest a rethinking of the work of authenticity for both educators and operators in nature tourism. This research has important implications for better understanding how visitors construct their perceptions of authenticity of wildlife and the implications for the ways in which wildlife tourism experiences and authenticity narratives are constructed in Anthropocene tourism.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".