Preference for Animals: A Comparison of First-Time and Repeat Visitors
Bibliographic record
Abstract
Wildlife tourism is one of the strongest-performing sectors in the global tourism market. While tourists’ preferences for and affection towards animals are a cornerstone of the industry, a better understanding of how experiences, including animal–tourist encounters and visitation frequency, influence visitors’ animal preferences is required. Through a comparison of preferences among first-time and repeat visitors of four species (giant panda “Ailuropoda melanoleuca”, red panda “Ailurus fulgens”, peafowl “Pavo cristatus”, and swan “Cygnus”), both before and after animal encounters at the Chengdu Research Base of Giant Panda Breeding (Panda Base), the results show that different species elicit varied and, at times, contrasting tourist preferences. As a result, animal preferences in wildlife tourism can vary based on different stages of visitation. Highlighting this dynamic relationship between animal preferences and visitation experiences is further elucidated through consumer learning theory and lively capital. The outcomes of this study contribute to a deeper grasp of human–animal interactions and have broader implications for the development of conservation programs in captive wildlife venues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".