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Record W2967642930 · doi:10.1007/s13280-019-01233-7

Did the movie Finding Dory increase demand for blue tang fish?

2019· article· en· W2967642930 on OpenAlex
Diogo Veríssimo, Sean C. Anderson, Michael F. Tlusty

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

VenueAMBIO · 2019
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsFisheries and Oceans Canada
FundersOxford Martin School, University of OxfordAssociation of Zoos and Aquariums
KeywordsCounterfactual thinkingFish <Actinopterygii>CredibilityWildlifeCharacter (mathematics)AdvertisingArtPsychologyFisheryEcologyBiologySocial psychologyPhilosophyBusinessMathematicsEpistemology

Abstract

fetched live from OpenAlex

Representations of wildlife in television and films have long been hypothesized to shape human-wildlife interactions. A recent example is Pixar's film Finding Dory, which featured a blue tang fish (Paracanthurus hepatus) as the main character and was widely reported in the popular press to have increased the number of such fish in the pet trade. We use Bayesian posterior predictive counterfactual models to evaluate the movie's effect on three metrics of societal behaviour. Although there was an increase in global online searches for the blue tang 2-3 weeks after the movie, we find no substantial evidence for an increase in imports of blue tang fish into the US, or in number of visitors to US aquaria compared to counterfactual expectations. It is vital that an evidence-based discourse is used when communicating potential impacts of popular culture on human-wildlife relationships to avoid loss of credibility and misdirection of conservation resources.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.030
GPT teacher head0.311
Teacher spread0.280 · 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