The provision of sustainable wildlife experiences
Bibliographic record
Abstract
Wildlife watching tourism is an increasingly important niche within nature-based tourism. As this niche continues to grow, care must be taken how tourists interact with the natural environment. One way to manage such interactions is through high-quality guiding, which has been acknowledged as a key element of wildlife watching tourism. The guide’s role was first conceptualized by Cohen (1985), who divided it into the instrumental, social, interactional, and communicative components. While several studies adopt Cohen’s framework, it is also argued that there are elements to nature guiding which are not fully covered (Randall & Rollins, 2009). In this chapter, we investigate the guide’s role in the context of wildlife watching tourism, using Cohen’s framework as a starting point. We compare musk ox safaris in Dovrefjell, Norway to Polar Bear tourism activities in Svalbard, Norway and Churchill, Canada using data from travel party interviews, participant observations and content analysis of online reviews. Findings reveal two additional components to guiding wildlife experiences: The uncertainty component and the encounter component. Based on these findings, we suggest an expanded model of the guide’s role when guiding wildlife watching tourism activities.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".