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Record W2472517935 · doi:10.1080/15528014.2016.1178548

Critical Eating: Tasting Museum Stories on Restaurant Menus

2016· article· en· W2472517935 on OpenAlexaffabout
Irina D. Mihalache

Bibliographic record

VenueFood Culture & Society · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMuseologyInterpretation (philosophy)DialogicExhibitionReading (process)Relation (database)TasteVisual artsSociologyMuseum informaticsMedia studiesArtPedagogyPsychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

This article explores exhibition-inspired themed menus in museum restaurants as a form of “critically engaged interpretation” (Meszros 2008). By proposing that themed menus in museum restaurants can connect some “interpretive communities” (Fish, 1980 Fish, S. 1980. Is there a Text in this Class? The Authority of Interpretive Communities. Cambridge, MA: Harvard University Press. [Google Scholar])—in this article, the focus is on foodies—with the museum content through food, the analysis reveals the forms of belonging, the hierarchies of taste and the interpretive modes which are afforded to museum visitors by culinary experiences. The article discusses the themed menus in relation to broader shifts in museum interpretation, informed by demands of the “new museology” (Vergo, 1989 Vergo, P. 1989. The New Museology. New York: Reaktion. [Google Scholar]) for inclusivity, dialogue and engagement. Critical in this transformation is the process of interpretation, based on a dialogic relation between the voices in the museum and the visitors. To be relevant to contemporary audiences, museums are providing various entry points to their collections, including culinary encounters. By engaging in a closed reading of themed menus developed in restaurants at the Art Gallery of Ontario (Toronto) and the Seattle Art Museum, the article discusses the interpretive practices developed around 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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.259
Teacher spread0.212 · 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 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

Citations24
Published2016
Admission routes2
Has abstractyes

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