A Review of Household Food Waste Generation during the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic may have impacted the quantity and composition of household food waste generation in parallel with recent changes to food behaviors. A literature review was undertaken to determine the state of household food wasting during pandemic circumstances. Forty-one articles that reported on household food waste generation during COVID-19 were identified. Most of these studies relied on self-reported recall of food wasting behavior (n = 35), primarily collected through surveys. The average total amount of household food waste generated during COVID-19 was 0.91 kg per capita per week. Average avoidable food waste generation was 0.40 kg per capita per week and average unavoidable food waste generation was 0.51 kg per capita per week. Fruit and vegetables were the most wasted types of food. Only five studies reported statistically significant changes (actual or perceived) to household food waste generation during COVID-19. These results indicate a possible decrease in total, perceived food waste generation during pandemic circumstances, with a possible increase in the actual generation of unavoidable food waste. Further research is needed to adequately determine the impact of the pandemic on household food waste generation, as the findings summarized in this review vary substantially and statistically significant results are limited.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".