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Record W4392354125 · doi:10.1080/13683500.2023.2280704

Tourism, animals & the vacant niche: a scoping review and pedagogical agenda

2024· review· en· W4392354125 on OpenAlexaff
David A. Fennell, Carol Kline, Muchazondida Mkono, Bryan S. R. Grimwood, Valerie Sheppard, Katherine Dashper, Jillian M. Rickly, Georgette Leah Burns, Giovanna Bertella, Erica von Essen, José-Carlos García-Rosell, Yulei Guo, Hin Hoarau-Heemstra, Álvaro López López, Gino Jafet Quintero Venegas, Patrick J. Holladay, Christina T. Cavaliere, Kellen Copeland, Brian Danley, Jessica Bell Rizzolo, Chris E. Hurst, Rie Usui, Mikko Henrikki Äijälä, Émilie Crossley, Kristine Hill, Michelle Szydlowski, Daniel Bisgrove, Samuel Blythe, Samuel R. Fennell, Sarah Oxley Heaney, Caroline Schuhmacher, Paul Tully, Sarah Coose, Jes Hooper, Rebecca Madrid

Bibliographic record

VenueCurrent Issues in Tourism · 2024
Typereview
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of WaterlooBrock University
Fundersnot available
KeywordsTourismScholarshipScope (computer science)Engineering ethicsCurriculumSociologyTourism geographyPolitical sciencePublic relationsEnvironmental ethicsPedagogyEngineering

Abstract

fetched live from OpenAlex

The topic of animal ethics has advanced in tourism studies since its inception in 2000, based on a diverse range of studies on species involvement, types of uses and contexts, level of engagement, states of animals, and theoretical perspectives. While there is still considerable scope to amplify research on animal-based tourism, a gap exists in tourism pedagogy amidst the field’s emphasis on a new expanding consciousness platform. We review the depth of existing scholarship on animal ethics in tourism and develop an agenda for advancing animal ethics pedagogy for the future. Our intent is to issue a call to action for curriculum committees, programme administrators, and educators to recognise and act on this critical moral domain in tourism education.

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)0.006
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0120.014
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.438
GPT teacher head0.581
Teacher spread0.143 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations15
Published2024
Admission routes1
Has abstractyes

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