Terroir? That's not how I would describe it
Bibliographic record
Abstract
Abstract Purpose – This article seeks to uncover if the definition of terroir is the same between the users (producers, vendors, high and low involvement consumers) of the term in the French wine industry. The objective is to uncover if the definition of terroir is homogenous between the user groups. Design/methodology/approach – An online questionnaire was distributed to an industry sample and then to a consumer panel, and asked respondents to outline in their own words how they would define a terroir product. Lexical analyses using SATO software were conducted and uncovered word frequency, distances, and contexts. Findings – The results show that each user group has its own taxonomy of terroir terms and uses an exclusive vocabulary. User group distinctions and commonalities are outlined. Globally it appears that the user groups seem to define terroir based on their level of involvement with wine as well as their role in the wine industry. Practical implications – French wine marketers can use these results to better understand how types of consumers perceive terroir and consider these perceptions when contemplating using terroir in a product description such as on wine labels or when developing marketing communications. Originality/value – Prior to this research there were no empirical results regarding how terroir is defined in the marketplace as well as the relationships between the descriptives used to define terroir. This research is a first step in understanding the value of terroir as a marketing attribute as well as the signals it represents for all user groups in the French wine industry.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".