How Many Latours Is Too Many? Measuring Brand Name Congestion in Bordeaux Wine
Bibliographic record
Abstract
Abstract Firms rely on brand names to market goods to consumers, and consumers rely on brand names to locate goods that satisfy their preferences. If multiple firms are using the same or similar names, consumers may be confused about which product to buy, and firms may not obtain the benefits of their investments in quality. Recently, both firms and scholars in a number of industries have expressed concern about brand name congestion—too many firms clustering around too few terms. This paper applies computational linguistic analysis to chateau names in the Bordeaux wine region to study the degree of brand congestion within a mature, traditional, and high-value market. We find that Bordeaux producers have highly similar names to one another, far more than in comparable wine regions such as California and Alsace. More than a quarter of all Bordeaux producers have a name that is identical or nearly so to at least one other producer, and many terms are claimed by dozens of different producers. Interestingly, however, we find that the most famous and renowned producers have names that tend to be more distinctive than their less famous brethren. (JEL Classifications: C88, D83, L66, O34)
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".