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Record W2886466038 · doi:10.1525/gfc.2018.18.3.28

Recipes as Culinary Communication in a Canadian Art Museum: Lobster Soufflé, Beef Stroganoff, and the Tensions of Gourmet Cooking in the 1960s

2018· article· en· W2886466038 on OpenAlexaffabout
Irina D. Mihalache

Bibliographic record

VenueGastronomica The Journal of Food and Culture · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRecipeNegotiationNarrativeBiographyMythologyInclusion (mineral)Visual artsMedia studiesSociologyArtAdvertisingHistoryArt historySocial scienceLiteratureBusiness

Abstract

fetched live from OpenAlex

This article proposes that recipes are a form of culinary communication, suggesting that a recipe's biography is one of communicative moments, negotiations, and multiple voices. This framework is applied to The Art of Cooking, a series of culinary demonstrations organized by the Women's Committee at the Art Gallery of Toronto in the 1960s. The events, featuring chefs such as James Beard and Dione Lucas, were organized around the logics of gourmet cooking but departed from it when faced with the realities of women's daily lives. The research is based on archival documents and media coverage of these very popular events, which offer an opportunity to explore the mythologies and narratives about gourmet cooking in the 1960s. This article argues that communications about a recipe are part of the recipe's evolving biography and need to be analyzed alongside ingredients, instructions, makers, and users. In addition, the article advocates for the inclusion of women's committees’ histories to those of art museums in North America.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.445
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.012
GPT teacher head0.210
Teacher spread0.199 · 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.

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

Citations0
Published2018
Admission routes2
Has abstractyes

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