Food for naught: Using the theory of planned behaviour to better understand household food wasting behaviour
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
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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