Online wine ecosystem: the digital narrative of Sangiovese
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate how grape varieties are narrated online by non-winery-owned sources in four countries: Australia, Canada the UK and the USA. This study focuses on Sangiovese, the most important varietal of Italy. Design/methodology/approach Texts collected on the Internet underwent a software-assisted semantic clustering procedure based on text-mining techniques. Identified clusters were then qualitatively analyzed by content. Findings The digital narrative on Sangiovese is mainly technical and conveyed by adopting a professional slant that is suitable for knowledgeable consumers but less effective for common and unexperienced wine drinkers. Online information is concentrated in few websites that act as information gatekeepers. Research limitations/implications The study contributes to the wine-related managerial literature on grape varieties, which are considered one of the most powerful factors in addressing consumer wine choice. Additionally, the investigation sheds light on the online wine ecosystem, by providing insights on how information is provided and the contents that are conveyed on the Internet. The findings of this study may be useful for Italian operators willing to promote Sangiovese-based wines in foreign markets. Originality/value Though explorative in nature, this study represents one of the first attempts to investigate the online narrative of grape varieties by presenting a marketing perspective and examining the characteristics of non-winery-owned online information which may shape wine consumers’ behavior.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".