Tell me what you waste and I’ll tell you who you are. An eight-country comparison of consumers food waste related habits
Bibliographic record
Abstract
Starting from an original survey conducted in eight countries in 2021 (Canada, China, Germany, Italy, Russia, Spain, UK, and USA), this research explores the relationship between household food waste and dietary habits in a cross-country comparative perspective. 8,000 questionnaires were recorded from samples representative of adult population of each country through an online survey conducted between the 13th and the 24th of August. The questionnaires were built on the work of Waste Watcher International Observatory on Food and Sustainability, an international observatory of social, behavioral and lifestyles dynamics behind household food waste. Relationship between per capita self-reported amount of food waste (expressed in kilocalories) and self-declared dietary habits (Traditional, Healthy and Sustainable, Vegetarian, Smart, Confused) was estimated using multiple linear regression models. Results show that Smart diets are associated with higher values of food waste in Canada, Spain, UK and USA. Vegetarian diets are associated to lower food waste values in China, Germany, UK and USA but not in Italy, Russia and Spain. Since the share of population adopting a Smart diet is on average 2.7% of the sample, interventions for food waste reduction should focus on this specific type of consumers, often associated to larger amounts of food waste.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".