Codes of Conduct at Zoos: A Case Study of the Chengdu Research Base of Giant Panda Breeding
Bibliographic record
Abstract
Zoos consistently implement codes of conduct in efforts to manage visitor behaviour. However, few studies have examined the use of the codes of conduct in zoos, even though they carry significant ethical implications regarding the relationship between humans and animals in society. This study provides an explorative investigation into the use of codes of conduct at the Chengdu Research Base of Giant Panda Breeding (Panda Base). Positioning the Panda Base as a place to negotiate the boundaries between humans and animals, this study surveyed visitors’ initial engagement with the Base’s code of conduct, their compliance with the code, and their assessment of the code. The findings point to a significant disparity between how visitors engage with and perceive the value of the code, which failed to prevent visitors from having close contact with animals at the Panda Base. We argue that Foucault’s philosophy on taboos in modern society can help us understand the ineffectiveness of the codes of conduct in zoos. However, Kant’s philosophy can orient human-animal interactions more ethically and provide an opportunity to consider the significance of codes of conduct in zoos. Suggestions for improving the effectiveness of codes of conduct at zoos are provided.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".