Enterprise diversity in the business of wine: what is a business case study?
Bibliographic record
Abstract
Purpose The purpose of this paper is to introduce the special issue of case studies in the enterprise diversity of wine business and to situate the wine business cases selected for this special issue, which feature a diversity of formats and approaches. Design/methodology/approach Rigour and relevance underpinned the choice of case studies for this special issue. All of the cases are informed by theory, and all share a common concern with the understanding of wine business phenomena and origins in or links to practice. Findings There is no consensus view on what a case is and what it is for in business research and teaching generally, and that this is appropriate given pluralistic approaches to teaching and research in the various business disciplines. Supporting this argument, the various types of cases encountered in the business literature are considered and an overview offered based on purpose, motivation, similarities and differences and common themes. Originality/value Each of the wine business cases presented in this special issue is situated within a typology, in which each offers a different approach to providing insight into the business of wine. The paper concludes with a discussion of current and future directions for business case studies and methods.
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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".