Qualifying tradition: Instituted practices in the making of the organic wine market in Languedoc‐Roussillon, France
Bibliographic record
Abstract
Abstract Within France, the Languedoc‐Roussillon region (now part of Occitanie) is home to about one third of the nation's area of certified organic vineyards. Each year, the world's largest organic wine fair, Millésime Bio, takes place in the city of Montpellier. This trade fair is an important site where organic wine is not only sold but also given meaning in the market, and importantly, differentiated from but made commensurate with conventional wine. In this paper, we examine processes and practices of ‘qualifying’ organic wine, including by means of relational processes of association and dissociation. Drawing on collaborative event ethnography and other qualitative methods, we focus on individual and institutional actors engaged in creating forms of commodified meanings that circulate with organic wine. In Languedoc‐Roussillon, these meanings reflect and reinforce a longer‐term so‐called shift to quality in wine production, yet also emphasize continuity over change, particularly through emphasis on ongoing role of artisanal, independent growers. We argue that qualification thereby works not only through association with independent growers but also by dissociation, specifically from Languedoc‐Roussillon's agrarian tradition of generic wine production and from the central role played by wine cooperatives in the social reproduction of the region's small‐holding grower class.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".