X‐it: Gen‐X and Older Wine Drinker Comparisons in New Zealand
Bibliographic record
Abstract
Some wine marketing studies make reference to the importance of Generation‐X as the next wave of wine drinkers, but draw attention to a glaring fact; this next generation is consuming less wine than national averages. Whilst considerable amounts of information about Generation‐X exist, few studies have addressed their underlying wine purchasing behaviours. A mock label for a red and white wine was developed and respondents were asked to indicate their probability of purchase and the price they would pay. A range of wine purchasing behaviour questions were included. A questionnaire was randomly presented in a mail survey to 1,144 New Zealand respondents drawn from a national wine mailing list (n=640) and an academic institution (n=504). No follow‐up was undertaken and a 28% response rate was achieved. Generation‐X wine consumers exhibited more differences than similarities to the older age cohort, with many differences being statistically significant. Whilst Generation‐X purchase wines in a similar fashion, they are mainly light purchasers of bottled wine. Generation‐X respondents showed a stronger likelihood of purchasing a never‐before‐seen wine and place a different emphasis on wine label information. More research on Generation‐X and their behaviours as wine consumers is required.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".