Does objective and subjective knowledge vary between opinion leaders and opinion seekers? Implications for wine marketing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wine is a heterogeneous, information-rich offering, with a plethora of brands in the market. Knowledge of wines amidst such diversity understandably varies. In addition, some offer opinions on wine while others seek them. Yet, the interplay between opinion leadership and opinion seeking, on the one hand, and wine knowledge, both objective and subjective, has received little attention by wine marketing researchers. Thus, this paper explores the relationships between opinion leadership and opinion seeking among wine consumers, and investigates whether objective and subjective knowledge varies between opinion leaders and seekers. An online survey was used to collect data on the four constructs and correlation analysis was undertaken to investigate the relationships between them. Key findings indicate that those who tend to seek opinions about wine tend not to have high objective knowledge of wine, as may be expected. On the other hand, opinion leaders think that they know about wine, and generally are objectively knowledgeable. Thus, their influence on others is not only based on communication, but on fact, representing a valuable source of influence for wine marketers. Understanding target consumers’ wine knowledge levels can potentially impact every aspect of wine marketing strategy.
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.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it