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Record W1967605594 · doi:10.1108/17511061011061676

Enterprise diversity in the business of wine: what is a business case study?

2010· article· en· W1967605594 on OpenAlexaff
Maureen Benson‐Rea, Victoria Little, Yvon Dufour

Bibliographic record

VenueInternational Journal of Wine Business Research · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTypologyRigourOriginalityBusiness caseDiversity (politics)Argument (complex analysis)Relevance (law)Business analysisValue (mathematics)New business developmentWinePhilosophy of businessBusiness modelSociologyMarketingManagement scienceKnowledge managementBusinessComputer scienceEpistemologyQualitative researchSocial scienceProcess managementPolitical scienceEconomics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.005
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.071
GPT teacher head0.356
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2010
Admission routes1
Has abstractyes

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