Benchmarking Giant Panda Welfare in Tourism: A Co-Design Approach for Animals, Tourists, Managers, and Researchers
Bibliographic record
Abstract
This study introduces a co-design benchmarking framework to understand tourists' perceptions of animal welfare, integrating diverse perspectives from tourists, researchers, and animals. By leveraging scientific theories to establish benchmark dimensions, the framework is refined through visitor input, ensuring a robust and adaptable methodological tool for assessing tourists' perceptions and animal informed consent in wildlife tourism. Using the Chengdu Research Base of Giant Panda Breeding as an example, we analyzed 4839 visitor comments collected from March to August 2023 to benchmark perceptions of giant panda welfare. This approach underscores the importance of effective communication in educational initiatives, aiming to enhance public literacy and knowledge about animal welfare. By addressing the complexity and variability in tourists' perceptions, the proposed framework contributes to more impactful conservation education efforts. The study demonstrates that a collaborative effort results in a benchmarking framework that is firmly grounded in theoretical foundations yet flexible enough to adapt based on visitors' insights and animal participation. Ultimately, this comprehensive approach ensures that educational initiatives resonate with tourists' diverse backgrounds, fostering a deeper understanding and commitment to animal welfare and conservation, which, we argue, should be key components of sustainable 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 | 0.246 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".