Making better use of what we have: Strategies to minimize food waste and resource inefficiency in Canada
Bibliographic record
Abstract
We examined the problems of and solutions to food waste through the main three frames of social science research on food waste: political economy; the cultural turn (the cultures, ideologies and politics of food and consumption); and political ecology. In the course of our collective research on food waste, we analyzed dozens of government and company documents, interviewed over 35 employees of food chain firms and organizations, including 9 middle to senior managers in food retail, and 2 farmers. One co-author, as part of this and affiliated work (McCallum, Campbell & MacRae, 2014), toured distribution facilities and stores of a major Canadian food retailer, had access to the Company’s head office staff, held group and one-on-one interviews with staff in a variety of capacities, and was granted access to confidential corporate reports. Another co-author volunteered with a food recovery organization and spoke with their operational staff. Our method to identify solutions is described in more detail below, but essentially we follow a normative approach as broadly outlined by MacRae and Winfield (2016). Our focus in this paper is on changes to policies, programmes and legislation/regulation at the level of the state. Such interventions are clearly only a piece of a wide ranging set of initiatives to be undertaken by numerous actors – from food chain firms to individual eaters – but our reading is that more attention has recently been paid to private firm than regulatory changes. We hope to redress this to some degree in this article.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.032 | 0.009 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".