Food Wastage in the Region of Waterloo, Ontario
Bibliographic record
Abstract
Much discussion on alleviating hunger and shaping more sustainable food production practices has focused on the production of food. More recently, an emerging body of literature has begun to focus on food wastage. Food wastage has direct and indirect environmental impacts, ranging from the unnecessary waste of inputs to produce food that will never be eaten, to the environmental impacts of the disposal of wasted food. In industrialized countries like Canada, an estimated 40 percent of food available for human consumption is discarded ¬– half of it from households. In spite of these numbers, only a handful of studies have begun to study food wastage in Canada. A better understanding of the mechanisms that drive up the food wastage levels in Canada is the first step needed to create targeted food wastage reduction strategies. \n\tThis study aims to answer the question: What factors drive Canadian households to waste food? A combination of online surveys, case study household food wastage collections, and case study interviews are used to gain a better understanding of the behaviours and socio-economic factors that shape household food wastage in Canada. \n\tThis study confirms many of the findings from other food waste research, but also emphasizes the role of food environments (e.g. retail environments and access to grocery stores) and environmental triggers (e.g. time constraints) in household food wastage. These findings highlight the complexity of the issue of food wastage, and the need for strategies that go beyond targeting household behaviours.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".