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Record W4243688153 · doi:10.32920/ryerson.14654862.v1

An Inventory and Analysis of Sustainable Food System Projects Implemented at Canadian Universities

2021· preprint· en· W4243688153 on OpenAlexfundaboutno aff
Ruvena I. Buslovich

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
FundersHEC MontréalConnaught FundUniversity of TorontoQueen's UniversityVictoria UniversityBrescia University CollegeLakehead UniversityTrent UniversityAlgoma UniversityYork UniversitySaint Paul UniversityBrock UniversityConcordia UniversityMcMaster UniversityUniversity of Ontario Institute of TechnologyKing's University CollegeHuron University CollegeBishop's UniversityNipissing UniversityUniversity of WaterlooWilfrid Laurier UniversityUniversity of WindsorUniversity of Ottawa
KeywordsBusinessEngineering managementProject managementMarketingKnowledge managementEngineeringComputer scienceSystems engineering

Abstract

fetched live from OpenAlex

The purpose of this project is to conduct an analysis of sustainable food system (SFS) projects implemented at Canadian universities. An inventory of SFS projects on Canadian university campuses was developed through a detailed content analysis of university websites. Gaps in the existing programs were explored through interviews with representatives from 40 of the 201 identified SFS projects. The interviews addressed project operations, definitions, motivations, approvals processes, challenges, lessons learned, project future, links to other projects, suggestions to other projects, and additional comments. These interviews found that even across different SFS project categories, there are strong common lessons and suggestions that can be applied to other existing or new projects, such as channeling passion into starting projects despite the obstacles faced, and building support networks. These results will help other SFS projects in their quest to address the environmental, social, and economic challenges faced by the parts of the food system: production, processing, access, distribution, consumption, and waste management.

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)0.006
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.427

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0210.037
Science and technology studies0.0070.002
Scholarly communication0.0040.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.013
GPT teacher head0.195
Teacher spread0.182 · 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 designObservational
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

Citations0
Published2021
Admission routes2
Has abstractyes

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