The artification of wine: lessons from the fine wines of Bordeaux and Burgundy
Bibliographic record
Abstract
Purpose In this paper, the authors argue that fine wines can be considered art and as such can be awarded luxury status. The authors discuss the processes of artification, through which such wines are recognized as art (Shapiro and Heinich, 2012), and heritagization, in which the cultural differentiation implicit in the concept of terroir (the various elements of a microclimate that contribute to a wine's specific attributes) connects a wine to its history and provenance. The investigation focuses specifically on fine wines from Bordeaux and Burgundy, which are renowned worldwide for their depth and flavors. What traits are intrinsic to the definition of art, and what social processes culminate in transforming an entity from nonart to art? Design/methodology/approach It is a conceptual paper that requires blending several viewpoints to present the authors’ own viewpoints. Findings This study aims to address the above questions and argues that fine wines, as a source of aesthetic pleasure, are themselves an art form. Research limitations/implications The implications for producers of fine wines and other artisanal products seeking to elevate brand awareness are discussed. Practical implications The findings of this study are of interest to wine scholars as well as wineries. They provide evidence as to how artification occurs. Originality/value While there are papers that address the issue of artification and heritagization individually, the authors bring to bear the importance of both concepts on specific wine regions in France: Burgundy and Bordeaux.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".