Eco-Feedback for Food Waste Reduction in a Student Residence
Bibliographic record
Abstract
Eco-feedback aims at increasing awareness of resource use to encourage conservation. A growing area of concern in sustainable living is food waste, and many new institutional waste receptacles incorporate waste sorting and recycling instructions for waste management. However, little attention has been paid to the design of encouraging awareness of waste in the home, particularly at the point of food waste. We explored the design challenges and effectiveness of novel eco-feedback techniques at the point of food waste through an in-situ study in a university residence. Our E-COmate system captures and visualizes domestic food waste data for more readily comprehensible and accessible information within a home environment embedded in an existing waste bin. Four E-COmate smart bins were introduced, deployed and evaluated for 8 weeks at a student residence in Canada. The aim of the study was to see whether a system like E-COmate could impact food waste patterns and awareness, and if so, to what extent it engages consumers. To explore its impact, a mix of methods was adopted. Waste audits were conducted to explore waste changes. Retrospective interviews were carried out to gain insights in residences' reflections and motivations. We show that E-COmate had a positive impact on participants' awareness of and behavior toward their food waste. Participants who had E-COmate installed in their kitchens showed overall a significant decrease in food waste and in particular a decrease of almost 32% in edible or once edible food waste, and a 69% decrease in generated compost waste during the last 2 weeks compared to the first 2 baseline weeks. Furthermore, while our control group showed an increase of 244% of waste of starches and grains toward the last 2 weeks (i.e., the end of term) compared to the 2 baseline weeks, the intervention group only showed an increase of 4.5% in waste of grains and starches. Eco-feedback further engaged residences in reducing food waste practices starting at the grocery store (e.g., by buying in smaller portions). In sum, eco-feedback as provided by E-COmate had positive impacts on reducing food waste. These findings are a result of increased awareness, the constant presence and immediacy of E-COmate served as a reminder, and their understanding of how much they actually waste as a group. Their awareness was reflected in how they adapted their shopping behavior as one way to reduce waste at home.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".