Messaging Matters? : Effect of Framing on Willingness to Reduce Food Waste
Bibliographic record
Abstract
This study explores the influence of message framing (positive, negative, or neutral) on willingness to reduce food waste among individuals affiliated with UBC Vancouver, including students, staff, families, and residents living on campus. Using Qualtrics, participants were randomly assigned to view one of three posters with different message framings around food waste. Then, they selected the sustainable options they were willing to adopt from an 11-item checklist, adapted from UBC’s Climate Action Plan 2030 (CAP 2030). We hypothesized that positively and negatively framed messages would increase willingness to reduce food waste compared to a control, and that positive framing would be more effective than negative. On the contrary, we found no significant differences between message conditions influencing participants’ willingness to reduce food waste behaviors. These findings suggest that one-time messaging may be insufficient to bring change. A CAP 2030-aligned initiative could instead emphasize repeated exposure to messaging, use more engaging content, and ensure greater visibility across campus, particularly in high-traffic zones such as dining halls and residence buildings. Such strategies could help strengthen message retention, enhance relevance, and willingness to change behavior. 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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".