MétaCan
Menu
Back to cohort
Record W4205459265 · doi:10.1080/15528014.2021.1992575

Making hamburgers healthy: plant-based meat and the rhetorical (re)constructions of food through science

2022· article· en· W4205459265 on OpenAlex
Jessica Mudry, Ryan J. Phillips

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFood Culture & Society · 2022
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRhetorical questionRhetoricSociologyFood systemsPolitical scienceSocial scienceFood securityGeographyAgriculture

Abstract

fetched live from OpenAlex

Prompted by the increasingly promoted “plant-based” burgers available on the market, this paper interrogates their epistemological and ontological implications, and how these food products problematize the discursive category of “meat.” Our analysis focuses on the promotional rhetoric of the “plant-based” meat company Beyond Meat; specifically, the Beyond Burger. Using the Beyond Burger, we address the scientific, technological, and socio-cultural bases upon which the concept of food functionality is rhetorically constituted and negotiated by public health officials, policy makers, and scientists. Ultimately, we argue that the Beyond Burger and other plant-based meats disrupt the established categorical boundaries of food.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.084
GPT teacher head0.343
Teacher spread0.259 · 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