Wine List Characteristics Associated with Greater Wine Sales
Bibliographic record
Abstract
Wine lists can be powerful merchandising tools that should be thoughtfully designed. Restaurant operators and observers have offered many suggestions regarding how to present a wine list to improve sales, but few direct tests of these notions have been published. Based on design and content attributes extracted from 270 wine lists from restaurants in several major metropolitan areas across the United States, this study evaluated the extent to which thirty wine-list characteristics coincided with higher wine sales. Overall, restaurants with higher wine sales tend to have wine lists that (1) are included on the food menu, (2) do not include a dollar sign ($) in the price format, (3) include more mentions of wine from a specific set of wineries, and (4) include a ?Reserve? category of wines. On the other hand, using ?Wine Style? as a major organizational category was associated with reduced sales. For casual dining restaurants specifically, higher wine sales were related to extensive wine lists that have a length of approximately 150 bottles of wine as compared to lists with fewer or more bottles, and with wine lists that offer more low-cost wines. Neither of these factors showed any effect in fine-dining restaurants.
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.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".