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Record W3210620696 · doi:10.15353/cfs-rcea.v8i3.520

Looking back on food studies in 2020-2021 in so-called Canada

2021· article· en· W3210620696 on OpenAlexaffvenueabout
Amanda Wilson, Meredith Bessey, Jennifer Brady, Michael Classens, Kirsten Lee, Charles Z. Levkoe, Jennifer Marshman, Tabitha Robin, Sarah-Louise Ruder, Phoebe Stephens, Tammara Soma

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsSimon Fraser UniversityWilfrid Laurier UniversityUniversity of WaterlooMount Saint Vincent UniversityUniversity of British ColumbiaUniversity of GuelphLakehead UniversitySaint Paul University
Fundersnot available
KeywordsPresidential addressState (computer science)Political scienceHistoryComputer sciencePublic administration

Abstract

fetched live from OpenAlex

In this collectively drafted Commentary, we offer some reflections on the past year for CAFS (Canadian Association for Food Stuides), and the state of food studies in general. Note: this is a modified version of the 2021 CAFS Presidential Address, given at the joint CAFS/ASFS/AFHVS/SAFN Conference Just Food: Because it never it just Food on June 10th, 20201.

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)0.033
metaresearch head score (Gemma)0.057
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.233
Threshold uncertainty score0.889

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.057
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.007
Science and technology studies0.0390.026
Scholarly communication0.0300.012
Open science0.0080.009
Research integrity0.0440.059
Insufficient payload (model declined to judge)0.0120.002

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.

Opus teacher head0.054
GPT teacher head0.241
Teacher spread0.187 · 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

Citations0
Published2021
Admission routes3
Has abstractyes

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