From Pets to Wildlife: An Ecological-Phenomenological Approach to Multispecies Tourism
Bibliographic record
Abstract
Pet ownership and wildlife tourism are two prominent ways people interact with non-human animals in contemporary contexts. Despite this, there is a need for further exploration of the interconnections between pets, wildlife, and visitors. Utilizing an ecological-phenomenological framework, this study examines how these multispecies interactions contribute to experiences that extend beyond the human domain. This research is based on a quantitative survey of 1422 participants at the Panda Center that were analyzed using inferential statistical methods to assess differences in visitor experiences. The statistical results reveal that pet ownership and wildlife encounters mediate the environmental affordances and constraints encountered by visitors, creating a dynamic and intricate nexus among pets, wildlife, and tourists. Specifically, pet ownership is shown to enhance both the richness and frequency of wildlife encounters. Nonetheless, the study highlights that human dominance over non-human animals remains a central environmental constraint in multispecies interactions despite the adoption of a more humane approach to animal management through 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".