Learning What Works: A Mixed-Methods Study of American Self-identified Food Conservers
Bibliographic record
Abstract
OBJECTIVE: Identify psychosocial factors influencing food waste mitigation and explore motivations and strategies for successful conservation among self-identified food conservers. METHODS: Mixed-methods study consisting of an online survey estimating food waste production and psychosocial factors and a focus group to explore waste mitigation strategies and motivations. RESULTS: Sampled 27 self-identified conservers (female, aged 18-30 years, White/Asian). Mean household food waste was 6.6 cups/wk (range, 0.0-97.9 cups/wk; median 1.3 cups). Reported waste mitigation strategies include proactive mitigation and adaptive recovery measures in each phase of the food management continuum. Conservers reported various intrinsic and extrinsic motivations to reduce food waste and viewed barriers as manageable. CONCLUSIONS AND IMPLICATIONS: Food conservers act on high intentions to reduce waste by consistently employing both proactive waste mitigation and adaptive food recovery measures. Future research is needed to determine if these findings hold in larger, more diverse samples and link specific behaviors to waste volume.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".