Bibliographic record
Abstract
Consumption ritual has been used to understand the meanings of consumption and consumer behavior, however less attention has been focused on the role of ritual in connoisseurship consumption and how consumption rituals can transform the consumer’s tastes. What is the role played by consumption ritual in connoisseurship taste?,Drawing on key concepts from ritual and taste theories and a qualitative analysis of the North American specialty coffee context, the authors address this question introducing the idea of connoisseurship taste ritual which is based on novelty coffee consumption practices that are opposite of the traditional or regular practices. The data collection set in the United States and Canada includes 15 consumer in-depth interviews, participant observation in 36 independent coffee shops in Canada and the United States, a Specialty Coffee Association of America event, and three barista coffee competitions. The body of qualitative data was interpreted using a hermeneutic approach.,The authors introduce the connoisseurship taste ritual which has several dimensions: (1) variation in the choices of high-quality products, (2) the place to perform the tasting, (3) the moment of tasting, (4) the tasting act, (5) perseverance, and (6) time and money investment.,This research paper extends the notion of consumption ritual introducing the connoisseurship taste ritual and also extends the theories of taste by explaining how, regarding a specific aesthetic category of product, people develop different tastes through ritualistic consumption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".