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Record W4405072133 · doi:10.3390/tourhosp5040073

To Touch or Not to Touch: Navigating the Ethical and Monetary Dilemma in Giant Panda Tourism

2024· article· en· W4405072133 on OpenAlexaff
Yulei Guo, David A. Fennell

Bibliographic record

VenueTourism and Hospitality · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsRegional Municipality of NiagaraBrock University
Fundersnot available
KeywordsDilemmaTourismInternet privacyBusinessPsychologyComputer sciencePolitical sciencePhilosophyLawEpistemology

Abstract

fetched live from OpenAlex

Tourists consistently demonstrate the need to touch wildlife, although policies often deny these experiences because of the psychological and physiological impacts on animals. However, philosophers contend that humans can learn to empathize with animals by feeling their way into the plight of animals through touch. Facing this dilemma, the paper asks if human touch can be ethically experienced in tourist interactions with animals by employing animal health warning labels. Using the case of “holding a panda” at the Chengdu Research Base of Giant Panda Breeding, Sichuan, China, the study investigates this dilemma through Johann Gottfried Herder’s philosophy on empathy and touch against the no-touch policies. A survey containing four scenarios shows that the use of payment can serve as a more effective tool than ethical appeal in reducing people’s decision to hold a panda through its inclusion of additional factors in the decision process. However, ethical touch building on animal health warning labels demands spaces for mutual respect, conservation awareness, and the recognition of health risks through a direct confrontation of the established emotional and sensual aesthetic appeal of cuteness between visitors and the panda. It is found that a combined use of payment and ethical appeal is necessary to restructure visitors’ willingness to hold a panda.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.377
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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