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Record W4400087238 · doi:10.1002/zoo.21847

Comparing the effectiveness of flagship species in zoo interpretation videos involving dialogic‐based narrative approaches

2024· article· en· W4400087238 on OpenAlexafffund
Jill Bueddefeld, Kevin C. R. Kerr

Bibliographic record

VenueZoo Biology · 2024
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsUniversity of GuelphWilfrid Laurier UniversityToronto Zoo
FundersMitacs
KeywordsOutreachCharismaDialogicFlagship speciesBiologyBiodiversityCitizen scienceNarrativeEnvironmental educationPublic engagementEnvironmental resource managementPublic relationsEcologySociologyPolitical scienceEndangered speciesHabitatPedagogy

Abstract

fetched live from OpenAlex

Zoological institutions frequently engage in indirect conservation activities as global conservation targets suggest a need for raising public awareness and engagement in biodiversity conservation. However, research suggests that while members of the public are typically aware of general conservation issues, they are often uncertain of simple and practical actions they take that will be impactful. In light of current conservation goals and targets, and the need for social science research to address the environmental learning and behavior change gap, this study builds upon prior action-based environmental education research and tests the efficacy of ex situ environmental education in supporting in situ conservation. Zoos typically employ flagship species to center their conservation messaging due to the purported draw of charismatic species. Using outreach videos with a dialogic-based narrative approach, we evaluated the efficacy of different flagships for conservation, comparing both species-focused versus generic conservation messaging and charismatic versus less charismatic species ambassadors. We found that zoo conservation outreach videos using dialogic-based narrative approaches were equally impactful regardless of the level of focus (i.e., species vs. broader biodiversity) or charisma level of the focal taxa.

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)0.001
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 score0.274
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.132
GPT teacher head0.334
Teacher spread0.202 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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