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Record W6982396246

The impact of animal welfare advertising on opposition to the Canadian seal hunt and willingness to boycott the Canadian seafood industry.

2013· article· en· W6982396246 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Archive @ University of South Florida (University of South Florida) · 2013
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOpposition (politics)BoycottWelfareFur sealAnimal rightsWarrant
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this research was to measure and compare the initial and carryover effects of a video advertisement developed by an animal welfare organization, namely Harpseals.org. The ad was designed to educate the public about an egregious act against wildlife (i.e., the Canadian seal hunt), increase opposition to this act, and recruit participation to boycott the industry (i.e., the Canadian seafood industry). After initial opposition to the egregious act had been measured, respondents were exposed to the ad, and subsequently asked again about their opposition to the seal hunt as well as their willingness to join the Canadian Seafood Boycott. About two months later, a follow-up study investigated whether the respondents' opposition to the seal hunt and their participation in the Canadian Seafood Boycott were still affected by the advertisement to which they had been exposed during the first contact. The results show that respondents' level of opposition to the seal hunt—even though it had somewhat leveled off in two months—was still significantly higher (42% higher) than before respondents had been exposed to the advertisement. The results further show that the single exposure to the ad increased boycott participation from 3.1% (as measured in December 2010) to 13.8% (as reported in February∕March 2011), an increase of 350%.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
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 studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.230
Teacher spread0.207 · 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.

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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueDigital Archive @ University of South Florida (University of South Florida)Same topicAnimal Behavior and Welfare StudiesFrench-language works237,207