A content analysis of influential wine blogs
Bibliographic record
Abstract
Purpose – The purpose of this exploratory study was to analyze the content of influential wine blogs. Design/methodology/approach – The study used content analysis software, Leximancer, to analyze the entire contents of five influential amateur wine blogs. Findings – A key finding is that these blogs all balance self-promotion with the content of their blogs, namely, wine and wine-related topics. The wine blogs, though evaluating wines in different ways, review not only the product attributes but also the experience surrounding wine. Research limitations/implications – Limitations of this study include that the analysis only included five wine blogs and the content analysis was conducted by a sole researcher using a computerized approach. Practical implications – Wine blogs have increasing influence in the wine industry, especially those written by amateur wine bloggers. As such, understanding the tactics used by wine bloggers is of interest to practitioners who aim to market their wines using such channels as well as providing insight into this contemporary platform for current and aspiring wine critics. Originality/value – This is the first content analysis study that analyzes the content of wine blogs as the readers themselves see it. It provides insights of value not only to those involved in marketing in the wine industry but also to those interested in the developments of amateur blogs in marketing.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".