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Record W2794576665 · doi:10.29311/mas.v15i3.2543

Art Museum Dining: The History of Eating Out at the Art Gallery of Ontario

2018· article· en· W2794576665 on OpenAlexaffabout
Irina D. Mihalache

Bibliographic record

VenueMuseum and Society · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsArt galleryIdeologyNarrativeVisual artsInclusion (mineral)MuseologyArtMedia studiesExhibitionSociologyPolitical scienceAnthropologyLawPoliticsLiterature

Abstract

fetched live from OpenAlex

Using archival materials from the Art Gallery of Ontario (AGO), this article recreates the culinary history of the art museum and advocates for the inclusion of food in the literature on art museum history and practice. The AGO, like many other North American art museums, has a rich culinary history, which started with dining events organized by volunteer women’s committees since the 1940s. These culinary programs generated a culinary culture grounded in gourmet ideologies, which became the grounds for the first official eating spaces in the museum in the mid-1970s. Awareness of the museum’s culinary history offers an opportunity to liberate the museum from prescriptive theoretical models which are not anchored in institutional realities; these hide aspects of gender and class which become visible through food narratives.KeywordsArt museum restaurants, culinary programming, women’s committees, multisensorial museums, Art Gallery of Ontario

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.364

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0230.010
Scholarly communication0.0060.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.000

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 head0.033
GPT teacher head0.209
Teacher spread0.176 · 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 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

Citations29
Published2018
Admission routes2
Has abstractyes

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