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Record W4253203187 · doi:10.32920/ryerson.14655036

Communicating Returnable Packaging Through Product Labelling

2021· preprint· en· W4253203187 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

Venuenot available
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Packaging Perceptions and Trends
Canadian institutionsTrent UniversityToronto Metropolitan University
Fundersnot available
KeywordsProduct (mathematics)BusinessMarketingPackaging and labelingSustainabilityPopulationBottleAdvertisingProcess (computing)Willingness to payEconomicsComputer scienceEngineeringSociologyMicroeconomics

Abstract

fetched live from OpenAlex

This research seeks to find effective ways to communicate returnable packaging campaigns to consumers through product labelling. This is an important line of inquiry as more and more countries are rolling out regulations that penalize companies for their wasteful practices. Knowing how to encourage people to engage with returnable packaging campaigns will be of great interest to future marketers and sustainability practitioners. This research uses experimental approach with the use of online questionnaires showcasing different label messages. Results show that the conventional method of tapping into the altruistic side of human nature with guilt-inducing messages is ineffective for the population at large. Embracing the self-enhancing, gain-seeking, pain-eliminating side of human nature results in a bigger pro-environmental behaviour change. Making the process of “doing the right thing” easier resulted in the higher willingness to return an empty milk bottle among participants when compared to financial rewards, social modelling, and justification.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.715
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.277
Teacher spread0.221 · 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