Mapping wine business research in the<i>International Journal of Wine Business Research</i>: 2007-2017
Bibliographic record
Abstract
Purpose The purpose of this paper is to systematically review the body of work featured in theInternational Journal of Wine Business Research(IJWBR) since its transition from theInternational Journal of Wine Marketing(IJWM) in 2007, and to assess the collective evolution of the topical structure of published research against the Journal’s aims as described in the inaugural editorial. Design/methodology/approach A scientometric study using both network analysis and narrative methods was used to evaluate the research contents of theIJWBR. Findings Results lead to four conclusions. Overall, the research published inIJWBRhas met the editorial aim of expanding beyond the marketing focus ofIJWM. Second, the Journal has become increasingly international in its approach to research activities, both in terms of authorship and sites of study. Third, the methods used in the study of wine business have advanced from descriptive univariate to more complex or predictive multivariate approaches. Finally, despite all of these desired advances, research grounded in marketing and consumer behavior perspectives still predominates the Journal. Originality/value This is the first review ofIJWBRto use a scientometric method; and this paper provides a description and assessment of progress made toward the publishing goals first envisioned for the Journal at its transition fromIJWMtoIJWBR.
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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 | 0.018 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.055 | 0.073 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".