“Clean Plates, Zero Waste” : An Examination of Anthropomorphic Figures and Positive Affect in Poster Design to Reduce Food Waste
Bibliographic record
Abstract
Introduction After successfully reducing back-of-house waste, the University of British Columbia (UBC) Food Services and UBC SEEDS are working to reduce food waste produced in UBC-Vancouver (UBC-V) residence dining halls. Research Question How do posters with an anthropomorphic figure and positive focus influence food waste production in a residence dining hall? Methods We analyzed previous studies on food waste reduction, behaviour change, and visual interventions, aiming to create a visual intervention promoting food waste reduction. The team designed a poster with minimal written information and a positive tone, hypothesizing that this would reduce food waste from plates. During a six-day baseline period at Place Vanier’s dining hall, Gather, the plate waste of diners was observed and rated on a 0-5 point scale from 5:00 to 7:00 pm. UBC Food Services collected the weight of plate waste in kilograms each day, which was used to augment our analysis. Posters were placed in various locations after this baseline period, and observations and kilogram data were collected in the same fashion. Results Results from our observational data showed a marginal difference in plate waste between conditions (U = 201,265.00, p = .05, rrb = 0.063); however, this was not corroborated by the kilogram data (U = 20.00, p = .95, rrb = -0.048). Though the hypothesis was partially supported, further studies are required. Recommendations We suggest that residence dining halls consider putting up posters based on the ideas of nudge influence, with a positive focus relating to the reduction of food waste. A future study also needs to be conducted to understand why students are so frequently tossing burgers, burger buns, and cakes. Finally, obtaining student feedback could allow the residence dining hall to make necessary adjustments to the menu, reducing potential food waste. 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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".