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Record W4224102905 · doi:10.15353/cfs-rcea.v9i1.490

Critical food guidance for tackling food waste in Canada

2022· article· en· W4224102905 on OpenAlexafffundvenueabout
Tammara Soma

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsSimon Fraser University
FundersPierre Elliott Trudeau Foundation
KeywordsFood wasteAgency (philosophy)HierarchyBusinessFood chainEnvironmental economicsFood systemsEnvironmental planningSupply chainEnvironmental scienceEngineeringWaste managementMarketingEconomicsFood securitySociologyGeographyAgriculture

Abstract

fetched live from OpenAlex

Food waste is a complex problem with far reaching negative environmental, social, and economic impacts. To identify appropriate solutions to address food waste, the food recovery hierarchy developed by the Environmental Protection Agency is currently the most popular guiding framework in food waste prevention and reduction. However, this paper found that the application and the interpretation of the guide is quite problematic due to its lack of consideration of scale in efforts to prevent and reduce food loss and waste. Furthermore, the food recovery hierarchy is premised on a linear food supply chain instead of a circular approach. Although the hierarchy recommends prevention as the most preferred approach, it still provides the option (albeit less preferred) to landfill food waste. Based on values and worldviews that potentially serve as better tools for food waste prevention and reduction, this paper explores the tensions within the food recovery hierarchy framework and identifies alternative critical food guidance developed in a Canadian social innovation lab.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.150
Threshold uncertainty score0.986

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0180.008
Scholarly communication0.0070.002
Open science0.0020.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.039
GPT teacher head0.239
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Food Studies / La Revue canadienne des études sur l alimentationSame topicFood Waste Reduction and SustainabilityFrench-language works237,207