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Record W4417074706 · doi:10.1093/biosci/biaf181

Wildlife Diversity in Global Team Sport Branding

2025· article· en· W4417074706 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBioScience · 2025
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsYork UniversityUniversity of Toronto
FundersH2020 European Research CouncilKoneen SäätiöAcademy of FinlandEuropean Commission
KeywordsWildlifeThreatened speciesBiodiversitySustainabilityWildlife conservationDiversity (politics)PopulationWildlife tourism

Abstract

fetched live from OpenAlex

Abstract Many sport organizations worldwide have capitalized on wildlife iconography to develop their brand. Given the ongoing global biodiversity crisis and the importance of sport in modern societies, representations of wildlife in the sport industry offer enormous potential for shifting social norms, raising funds and promoting biodiversity conservation initiatives within the industry itself. We collected data on professional teams that use wild animals either in their name, logo, or supporters’ nicknames across 50 countries and across 10 team sports. We identified 727 sport organizations using wildlife iconography or nicknames. Mammals and birds are the most represented classes, and lions (Panthera leo), tigers (Panthera tigris), and grey wolves (Canis lupus) are the most frequently selected species. Threatened species and species with a declining population trend are more represented than other species, with differences across regions. This is a critical first step toward integrating biodiversity conservation in the sustainability agenda of sport organizations.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.147

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.335
Teacher spread0.298 · 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