Bibliographic record
Abstract
Some wine marketing studies make reference to the importance of wine labels and the information they contain. Others suggests that the information content of wine labels be grouped under seven information positioning statements: namely, parentage, nonpareil, manufacture, attributes, endorsements, end user and end use. Nested within some of these statements is other information commonly associated with wine lables. There is a dearth of research that examines the importance of these seven statements or their expanded state. A questionnaire, exploring the importance of an expanded list of information elements and the importance of front and back labels, was constructed. As these questions formed part of a larger research endeavour, eight versions and two wine types were presented in a mail survey to 1.144 participants. The survey sample was drawn from a national wine mailing list (n=640). plus staff (n=304) and students (n=200) of an academic institution. No follow‐up activity was undertaken and a 28% response rate was achieved. A range of behavioural and demographic information was collected. Using a 7‐point scale, respondents were asked to indicate how important 14 pieces of information were to them in deciding on which wine to buy. Varied and significant levels of importance exist for some elements of wine label information. For example, front labels were found to be more important than back labels, and this is supported by significant differences amongst some background information. The expansion of parentage into its component parts shows wine company and brand name to be more important than history of wine maker or history of wine region . The results of this research challenge a number of existing findings and beliefs on the importance of various elements of wine label information.
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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".