Framing to Reduce Single-Use Cups
Bibliographic record
Abstract
Introduction Our study aims to find the effect that loss and gain framing has on changing consumers’ selfreported intent to reduce the use of single-use cups. Research Question What is the effect that loss and gain framing have on changing consumer’s self-reported intent to use single-use cups? Methods Using a Qualtrics survey, participants were assigned to either the control condition (a neutral statement that offers no insight into the experiment's focus), the loss framing condition (which focuses on the idea of losing money when buying a single-use cup), or the gain condition (focuses on the idea of saving money when not buying a single-use cup). We then asked them to complete a survey where a Likert Scale measures likeliness to examine our primary measure of consumer intention to reduce single-use cup usage and our secondary measure looking at intent to bring one’s reusable mugs. A Likert Scale measuring surprise was also used to investigate how much people know about the cup fee as a tertiary measure. Results There was no significant difference in the gain, loss, and neutral frame in our primary measure, but significant effects in our secondary and tertiary measures. Although framing does not affect consumers’ intention to reduce single-use cup usage, loss framing increases intentions to bring reusable cups, and both gain and loss framing can increase surprise about the 25-cent fee. Recommendations We believe that UBC should implement posters, particularly in high-traffic areas where individuals are more likely to make purchasing decisions related to beverages (i.e.., tills, entrance, etc.), combining the loss framing aspect of our research with the long-term financial consequences associated with purchasing a single-use disposable coffee cup ($100 lost over a year). Future research could explore the effects of framing in person at cafés and the effects of financial versus environmental implications of single-use cups. We also recommend exploring partnerships with service providers on campus that could more conveniently offer alternatives to single-use cups. 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.”
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".