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Record W2135088720 · doi:10.1177/1077800410366939

Performing Taste at Wine Festivals: A Somatic Layered Account of Material Culture

2010· article· en· W2135088720 on OpenAlexaff
Phillip Vannini, Guppy Ahluwalia-Lopez, Dennis D. Waskul, Simon Gottschalk

Bibliographic record

VenueQualitative Inquiry · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsTasteScholarshipAestheticsRepresentation (politics)WineReflexivitySociologyPsychologyArtSocial scienceVisual artsPolitical science

Abstract

fetched live from OpenAlex

Sensuous scholarship refers to research about the human senses, through the senses, and for the senses. Sensuous scholarship asks us to recognize the meaningfulness of our somatic experience of the world, to understand the skilful activities through which we actively make and remake the world through our senses, and to develop evocative strategies of representation— to write sensuously. In this article, the authors reflect on one particular genre of sensuous scholarship, which they refer to as the somatic layered account. The authors draw upon participant observation data collected at wine festivals at seven sites scattered across western British Columbia and Southern California. The authors examine how people express taste sensations and preferences to others, as well as what role wine’s material properties play in these social dramas. In formulating and developing the concepts of somatic accounts, taste vocabularies, and somatic joint acts, the authors contribute to a growing understanding of the social aspects of the senses and of sensations, as well as how people perceive the material world—and the sense of taste in particular—in active and reflexive ways.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.023
Scholarly communication0.0060.004
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.441
Teacher spread0.357 · 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

Citations38
Published2010
Admission routes1
Has abstractyes

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