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Record W4250425495 · doi:10.1504/ijbg.2019.099306

Producing good wine just is not enough: the role of management in building a competitive industry cluster

2019· article· en· W4250425495 on OpenAlex
Svan Lembke, Lee Cartier

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Business and Globalisation · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsOkanagan CollegeOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMultinational corporationCompetitive advantageCluster (spacecraft)MarketingBusinessBusiness clusterIndustrial organizationMultitudeWineCluster developmentEconomic geographyEconomicsComputer scienceEngineeringPolitical science

Abstract

fetched live from OpenAlex

The wine industry is a central part of the agricultural products cluster in British Columbia. Findings from a range of applied research projects indicate that the cluster may have hit a glass ceiling in terms of its development. A better understanding of cluster dynamics and management processes is needed to assist the cluster grow to beyond its initial wave of success, build more refined decision-making processes and competitive advantage. This takes the research findings and present literature beyond the descriptive style of economic analysis and introduces causality as well as concepts from business strategy to the institutional and the business layers within the cluster. The purpose is to deliver a new direction for rural clusters that are defined by a multitude of small and mid-size firms acting without the influence of large mature multinational corporations and having outgrown the first generation of entrepreneurial cluster leaders.

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.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.243
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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