Reading between the vines: analyzing the readability of consumer brand wine web sites
Bibliographic record
Abstract
Purpose Many audiences might view wine brand web sites as complex or unapproachable. Wine drinking is no longer a pastime of the affluent and elite; rather, it is increasingly popular among younger consumer groups and those from broader socio‐economic backgrounds. In order to communicate effectively with newer consumer demographics, wine brand web sites must first and foremost be understandable and readable. The purpose of this paper is to investigate this issue, aiming to answer the question of whether the web sites of popular wine brands are readable or not. Design/methodology/approach To investigate the readability of consumer brand wine web sites, web site copy from the 20 most popular wine brands in the USA was calculated across multiple readability indices employing content analysis. Findings The findings suggest that, while certain target demographics may be assumed by grouping wine brand web sites based on readability measures, there are marked differences in readability across wine web sites of a similar nature that only serves to reinforce consumer confusion, rather than help remove it. Originality/value There is scant literature on readability in the wine business literature in general, and with regard to the readability of wine web sites in particular. The research highlights the need for those who communicate with a broad audience of wine consumers to give attention to web site content, and readability in particular. This is for two reasons: less sophisticated consumers will not respond to wine marketing messages they cannot understand, and more sophisticated wine drinkers will react more positively to messages that are clear and well‐written. Readability is equally important for these more sophisticated consumers.
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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".