Bibliographic record
Abstract
Wildlife festivals are short-term public celebrations of local wildlife features. Despite claims that wildlife festivals are examples of ecotourism, there are few studies to confirm this link. The goal of this study was to examine how consistent the objectives, strategies, and outcomes of wildlife festivals are with ecotourism principles, and whether those objectives, strategies, and outcomes are in alignment. We surveyed 54 organizers of wildlife festivals in Canada about their festivals’ emphases on objectives and strategies related to ecotourism and conservation. Six years later, we asked the same organizers about their festival’s impact on sustainability outcomes. Only 24% of organizers labelled their festivals as ecotourism events. Festivals emphasized experiencing nature and providing learning opportunities over environmental, economic, or social sustainability outcomes. Ecotourism principles were supported by festival objectives, but less so by strategies and outcomes. There was some alignment between festival objectives and outcomes, but less between objectives and strategies and between strategies and outcomes. These results contribute to higher-level organizational analyses of nature-based tourism by critically reflecting on the relationships among goals, strategies, and outcomes. Wildlife festival organizers should more clearly identify their objectives, communicate with other festivals, place more emphasis on sustainability, and make their objectives, strategies, and outcomes more consistent.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".