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Record W4378070624 · doi:10.5304/jafscd.2023.123.010

Food systems change and the alternative campus foodscape

2023· article· en· W4378070624 on OpenAlexaffabout
Michael Classens, Kaitlyn Adam, Sophia Srebot

Bibliographic record

VenueJournal of Agriculture Food Systems and Community Development · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsTrent UniversityUniversity of Toronto
Fundersnot available
KeywordsScholarshipFood systemsWork (physics)ProcurementPolitical scienceSociologyUniversity campusPublic relationsLibrary scienceBusinessMarketingGeographyComputer scienceEngineeringFood securityAgriculture

Abstract

fetched live from OpenAlex

Postsecondary students, staff, and faculty across North America are actively involved in trans­forming food systems on campuses and beyond. Much of the scholarship documenting these inroads has focused on procurement, production, and pedagogy. While this work is essential, it paints an incomplete picture of the ways postsecondary campuses—and students in particular—are contributing to realizing more just and sustainable food systems. In this paper, we elaborate the contours of what we propose as the alternative campus foodscape in Canada by highlighting campus food systems alternatives (CFSAs), which we define as on-campus initiatives that are moti­vated by animating structural, practice, and/or policy change through the campus foodscape. We demonstrate how CFSAs are distinct from conven­tional food systems and argue that they are essen­tial elements of a robust movement for food systems transformation.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.579
Threshold uncertainty score0.847

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0130.033
Scholarly communication0.0100.003
Open science0.0010.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.042
GPT teacher head0.213
Teacher spread0.171 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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