Civilised Panda viewing and visitor restrictions: an ethics of care approach to managing tourist misbehaviour
Bibliographic record
Abstract
Introducing measures to regulate tourist misbehaviour at visitor attractions is an ongoing challenge. While previous research has explored the factors behind visitor misconduct, little attention has been given to how these measures are received and interpreted by the public. This study addresses this gap by investigating how restrictions to misbehaving visitors influence public reactions through the lens of the ethics of care. This framework is particularly relevant in China, where ‘civilised tourism’ has become a national discourse promoting responsible tourist behaviour. Using theme and sentiment analysis, the study examines 319 comments from 27 public announcements on WeChat posted by the Chengdu Research Base of Giant Panda Breeding following reports of tourist misbehaviour. The findings suggest that such restrictions elicit collective moral reinforcement towards misbehaviours, with WeChat users actively endorsing the measures to uphold civilised tourism norms. By integrating the ethics of care into discussions on tourist misbehaviour, this research underscores the importance of fostering responsible human-animal interactions and civil behaviours. Given China's increasing emphasis on wildlife conservation and the growing role of social media in shaping public discourse, this study aligns with China's national strategy of civilised tourism, offering insight into how science-based governance enhances public engagement in wildlife protection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".