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Record W4386778718 · doi:10.1080/10496505.2023.2255730

Agriculture-Related Courses in Canadian Universities: How Course Outlines Differ between Social Science and Science Faculties

2023· article· en· W4386778718 on OpenAlexaffabout
Amy Campbell, Megan Versteegh, H. Ward McGraw, Sharan Riar, S. Steele, Lisa Mardlin-Vandewalle, June I. Matthews

Bibliographic record

VenueJournal of Agricultural & Food Information · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsAgricultureSustainabilityFood securityFood sovereigntyIndigenousAgricultural scienceSociologyPolitical scienceEcologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

The university faculties that offer agriculture-related courses and how agricultural information is provided on course outlines are unknown. From June 2020 through April 2022, Canadian university websites were searched using the terms agri*, food*, farm*, crop*, plant*, animal*, and sustain*, retrieving 417 course outlines in 23 social science and 12 science faculties. Through descriptive statistics and qualitative analysis, the authors identified six themes: industrial agriculture and biotechnology; sustainability; alternatives and organic agriculture; food sovereignty, Indigenous, and gender; food security and food safety; and animal ethics and welfare. Instructors are encouraged to broaden the agricultural topics presented and provide balanced information.

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)0.005
metaresearch head score (Gemma)0.027
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: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.387

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.009
Science and technology studies0.0040.002
Scholarly communication0.0050.002
Open science0.0020.004
Research integrity0.0010.001
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 head0.022
GPT teacher head0.246
Teacher spread0.225 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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