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Record W4383533235 · doi:10.1080/02508281.2023.2226038

Empathy in animal-based tourism contrasting constructed care and care ethics at a captive wildlife venue

2023· article· en· W4383533235 on OpenAlexaff
David A. Fennell

Bibliographic record

VenueTourism Recreation Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsBrock University
Fundersnot available
KeywordsEmpathyWildlifeWildlife tourismTourismMedical tourismPsychologyEnvironmental ethicsEcotourismWildlife conservationEnvironmental resource managementGeographyBusinessPublic relationsEnvironmental planningPolitical scienceSocial psychologyEcologyBiologyEconomicsArchaeology

Abstract

fetched live from OpenAlex

The aim of this paper is to introduce the work of the eighteenth-century German philosopher Johann Gottfried Herder on empathy to contemporary tourism. Animals and humans share a language of nature, which was an essential point of departure for Herder in arguing that the sensual must be elevated alongside the empirical in arriving at deeper truths. Herder’s philosophy is used as a benchmark to examine empathy-based programmes at the Seattle Aquarium, which also speaks of a language of nature through its animal-based empathy programming. A synthesis of these two bodies of knowledge, and conceptual framework, uncovers several comparatives over the ethos, pathos and logos of empathy at captive animal venues, where we must reconcile use as a function of institutional realities and the constructed care that goes into the facilitation of animal-human interactions.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.029
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.081
GPT teacher head0.434
Teacher spread0.353 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations10
Published2023
Admission routes1
Has abstractyes

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