Tell Me What You Waste and I’ll Tell You Who You Are: An Eight-Country Comparison of Consumers’ Food Waste Habits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using an original survey conducted in eight countries in 2021 (Canada, China, Germany, Italy, Russia, Spain, the UK, and the USA), this study explored the relationship between household food waste and dietary habits through a cross-country comparative perspective. In total, 8000 questionnaires were recorded from samples representative of the adult population of each country through an online survey conducted between the 13th and the 24th of August. The questionnaires were developed from the Waste Watcher International Observatory on Food and Sustainability, an international study of the social, behavioral, and lifestyle dynamics behind household food waste. The relationships between the per capita self-reported amount of food waste (expressed in kilocalories) and self-declared dietary habits (traditional, healthy and sustainable, vegetarian, smart, and confused) were estimated using multiple linear regression models. The results showed that smart diets are associated with higher values of food waste in Canada, Spain, the UK, and the USA. Vegetarian diets are associated with lower food waste values in China, Germany, the UK, and the USA, but not in Italy, Russia, and Spain. The share of the population adopting a smart diet was, on average, 2.7% of the sample; therefore, interventions for food waste reduction should focus on these specific types of consumers, who are often associated with larger amounts of food waste.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it