Food Waste Reduction: A Test of Three Consumer Awareness Interventions
Bibliographic record
Abstract
Halving food waste by 2050 as per the Sustainable Development Goal 12.3 is key to securing a food system that is sustainable. One approach to reducing household food waste is through education campaigns. We recruited 501 households divided into three types of intervention groups and compared with a control group to better understand the efficacy of diverse education campaign approaches. Food waste interventions included a passive approach (handouts), a community engagement approach, and a gamification approach. We conducted waste audits, household surveys (pre- and post-intervention), and a focus group at the end of the campaign. The passive and gamification groups had similarly high levels of participation, while participation in the community group was very low. The passive group and the gamification group had higher self-reported awareness of food wasting after the campaign and lower food wastage than the control group. Waste audits found marginally significant differences between the game group and the control (p = 0.07) and no difference between the other campaign groups and the control group in edible food wasted. Frequent gamers were found to generate less edible food waste than infrequent gamers. We conclude that the evidence about the potential for gamification as an effective education change tool is promising and we recommend further study.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".