Through Ups and Downs : The Effect of Real-Time Feedback on Food Waste Behavior in a University Dining Hall
Bibliographic record
Abstract
Introduction UBC Vancouver’s all-access dining model implemented in 2022 has presented challenges to reducing post-consumer food waste. While informational feedback has been widely used to combat food waste behavior, there is a lack of research on the combined effects of informational feedback with other forms of interventions. Consequently, as part of UBC’s goal to reduce food waste by 50% by 2030, our study examines how the combination of informational feedback and normative prompt can influence post-consumer food waste. Research Question How does signage displaying daily fluctuations in food waste (percent change) affect the total food waste weight in kilograms at an all-access dining hall? Methods We designed signs placed in three locations at Open Kitchen displaying daily food waste percentage change along with a prompt to reduce food waste. Our condition 1 is when there is a displayed decrease, and condition 2 is when there is a displayed increase. Over 14 days, food waste data from Open Kitchen was collected to update the percentage change in food waste. Results Results show that combining a normative prompt and feedback effectively reduces food waste at Open Kitchen, specifically by 40.5%. Furthermore, when comparing the effectiveness of a displayed decrease and a displayed increase, there are no statistically significant differences in food waste behavior. Recommendations We recommend that UBC dining halls display waste data in the three first year dining halls: Gather, Feast, and Open Kitchen. We also recommend that UBC continue collecting waste data so that future research can utilize a larger data sample which accounts for time-of-year as a confounding factor. UBC dining halls may also benefit from tracking waste in relation to dining options and adjusting their menus accordingly. Finally, we recommend the implementation of a food waste tracking system, such as LeanPathTM, that provides data collection tools and analytics across UBC’s dining halls. 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 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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".