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Record W2794303521 · doi:10.15353/cfs-rcea.v5i1.308

Filling our plate: A spotlight on feminist food studies

2018· article· en· W2794303521 on OpenAlexafffundvenueabout
Jennifer Brady, Barbara Parker, Susan Belyea, Elaine Power

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Gender and Feminism Studies
Canadian institutionsMount Saint Vincent University
FundersUniversity of Waterloo
KeywordsPleasureSociologyMedia studiesFeminismWork (physics)Space (punctuation)Gender studiesFeminist theoryPolitical sciencePsychologyEngineering

Abstract

fetched live from OpenAlex

The idea for this special issue emerged from the enthusiastic response to a day-long series of sessions on feminist food studies that were held during the joint conference of the Canadian Association of Food Studies, the Association for the Study of Food and Society, and the Agriculture, Food, and Human Values Society, in 2016, in Scarborough, Ontario. The sessions brought together feminist food scholars from across Canada and the U.S. to share their work and to collectively claim space within the conference program to address feminist perspectives in food studies. For us, and the many presenters and attendees at the sessions, the opportunity to gather together and savour more than the usual one or two conference sessions devoted to feminist perspectives was a long-awaited pleasure that did not disappoint. The presenters and audience members illuminated many of the issues, complexities, and perspectives that an explicitly feminist lens brings to food studies. The energy and excitement that infused the room as each presenter shared their work filled our plates that day.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
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 head0.121
GPT teacher head0.327
Teacher spread0.206 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations7
Published2018
Admission routes4
Has abstractyes

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