A great or heinous idea?: Why food waste diversion renders policy discussants apoplectic
Bibliographic record
Abstract
Comprehensive agri-food policy includes food waste reduction as an important policy goal. Food bank donor indemnification is codified in law in several countries to support the charitable food sector. Other policy instruments to address waste reduction have emerged recently, among them tax measures to incentivize private sector action. These have been increasingly linked to the discourse on hunger in many jurisdictions. We asked 17 Canadian food insecurity policy entrepreneurs to comment on a vignette scenario featuring a hypothetical proposal for food waste diversion as a policy response to household food insecurity; the polarization in responses – ‘Not human garburators,’ vs. ‘everyone wins’ – was remarkable. This case of an unexpected divergence in the response to a policy idea in the food insecurity realm provides an opportunity to understand fundamental differences in societal perspectives that might be relevant to public health more generally. In particular we address the parallel humanist and ecological imperatives at work in contemporary public health practice. It appears that objections to food waste diversion for human consumption could be crystallized in terms of Mary Douglas’s theory of morality within particular worldviews. From a humanist viewpoint, the proposal was an indignity; for those with an ecological worldview, it was sensible and pragmatic. Public health has embraced ecosystem thinking and takes for granted that its ecological approach is sensitive to humanist perspectives but the two worldviews may differ and humanist ideals of dignity may raise moral rancor when considered in the context of ecological good.
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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 | 0.026 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.054 | 0.045 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".