Terroir? That's not how I would describe it
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it