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Record W6960754547 · doi:10.14288/1.0444865

Fin-tastic Alternatives : The Impact of Animal Image and Messaging on Single-cup Use

2024· article· en· W6960754547 on OpenAlexaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsLikert scaleScale (ratio)Affect (linguistics)Liberian dollarSocial mediaImage (mathematics)Control (management)

Abstract

fetched live from OpenAlex

Introduction Our study aimed to measure the effects of exposure to an orca image and a short text depicting marine consequences on individuals' behavioral intentions to purchase single-use cups. Research Question How does an animal image and a short text affect people’s intention to use single-use cups? Methods Participants (N=252) ranging from ages 16 to 69 were randomly assigned into one of the three conditions: image + text condition, text condition, and blank cup (control condition). We hypothesized that participants in the image + text condition would be less inclined to purchase the single-use cup in our study and were willing to pay less than the other conditions. We used a 7-point Likert scale measuring their willingness to purchase the cup and a scale ranging from 0 to 1 Canadian dollar regarding their willingness to pay. Results We found that participants in the text condition were less inclined to purchase the single-use cup than the control condition but not the image + text condition. There was no significant difference in the amount of money participants were willing to pay for the cup, which contradicted our hypothesis. This may be due to the study being conducted online and participants finding the orca image appealing. Recommendations Enhancing social media platforms regarding single-use cup impacts and promoting 5S principles could increase sustainability on campus. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.001

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 head0.020
GPT teacher head0.203
Teacher spread0.183 · 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 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
Published2024
Admission routes1
Has abstractyes

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