Who Will Replace Parker? A Copula Function Analysis of Bordeaux<i>En Primeur</i>Wine Raters
Bibliographic record
Abstract
Abstract The influence of the wine rater Robert Parker Jr. on Bordeaux wine extended over a 40-year period, with a particular impact on en primeur wine prices. Consequently, his announcement in 2015 that he would no longer rate en primeur wines creates some uncertainty for many chateaux that have purposely designed their production with his palate and preferences in mind. Although the wine rater Neal Martin was named by Parker to be his successor in terms of en primeur wine ratings, there are several other wine critics who have consistently rated en primeur wines over several years. Consequently, we employ copula function analysis to explore which wine critics’ ratings exhibit the closest linear and nonlinear relationship, for right bank en primeur wines, with those of Parker. The study employs data over the period of 2005 through 2012, during which time several wine critics, including Neal Martin for the period of 2010–2012, rated en primeur wines alongside Parker. Our results indicate that of the wine critics that continue to rate en primeur wines, the ratings of James Suckling exhibit the highest rank correlation and also bivariate upper tail dependence, identified through copula function analysis, with those of Parker. (JEL Classifications: C19, G13, L66)
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".