Food for naught: Using the theory of planned behaviour to better understand household food wasting behaviour
Bibliographic record
Abstract
Abstract To better understand food wasting behaviour, the theory of planned behaviour was used to inform the development of a survey which was administered to households in London, Ontario, Canada. Respondent households (n = 1,263) threw out avoidable food waste 4.77 times/week (SD = 4.81, Mdn = 4.0) and 5.89 food portions/week (SD = 5.66, Mdn = 4.0). When asked to choose one of three possible motivators to reduce food wasting behaviour, 58.9% selected reducing monetary loss as their first choice and this was significantly (p < 0.001) higher than both reducing environmental impact (23.9%) and reducing social impacts (17.2%). A linear hierarchical regression analysis (R 2 = 0.30, p < 0.001) on intention to avoid food waste demonstrated that perceived behavioural control (p < 0.001) and personal norms (p < 0.001) had the greatest positive impact on intention. A linear hierarchical regression analysis (R 2 = 0.32, p < 0.001) on self‐reported food wasting behaviour showed that perceived behavioural control (p < 0.001) and personal attitudes (p < 0.01) resulted in less food wasting behaviour, while more children in a household (p < 0.01) resulted in more food wasting behaviour. Interventions that seek to strengthen perceived behavioural control and convey the monetary impact of food waste could help reduce its disposal.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".