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Record W2766897003 · doi:10.1590/s0034-759020170506

THE TASTE TRANSFORMATION RITUAL IN THE SPECIALTY COFFEE MARKET

2017· article· en· W2766897003 on OpenAlexaffabout
Ronan Torres Quintão, Eliane Pereira Zamith Brito, Russell W. Belk

Bibliographic record

VenueRevista de Administração de Empresas · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsYork University
Fundersnot available
KeywordsTasteConsumption (sociology)EthnographyParticipant observationContext (archaeology)SociologySpecialtyAestheticsConsumer researchField (mathematics)Field researchAdvertisingPsychologyAnthropologyArtSocial scienceHistoryBusiness

Abstract

fetched live from OpenAlex

ABSTRACT Although the consumer culture field has addressed the role of ritual processes in consumption, no research has yet identified how connoisseur consumers, through ritual practices, establish and manipulate their distinction from other consumers. Drawing on key concepts from ritual theory, this research addresses the role played by ritual in connoisseurship consumption and consumers’ taste. In conducting an ethnographic study on connoisseurship consumption, the first author immersed himself in the North American specialty coffee context-Toronto, Montreal, Seattle, and New York-from August 2013 to July 2014. He used long interviews and participant observation to collect data, which was then interpreted using a hermeneutic approach. We introduce the taste transformation ritual, theorizing the process that converts regular consumers into connoisseur consumers by establishing and reinforcing differences between mass and connoisseurship consumption. We develop a broader theoretical account that builds on consumption ritual and taste formation.

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)0.004
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.301
Teacher spread0.262 · 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

Citations67
Published2017
Admission routes2
Has abstractyes

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