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Record W2042034060 · doi:10.1080/08109028.2014.933600

Institutional design matters: institutional causes of the Brazilian wine industry’s poor performance

2013· article· en· W2042034060 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePrometheus · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsSimon Fraser University
FundersUniversity of British ColumbiaSimon Fraser UniversityGenome British ColumbiaGenome Canada
KeywordsInstitutionalisationWineTriple helixGovernment (linguistics)BusinessState (computer science)Institutional theoryEconomic systemIndustrial organizationEconomicsPolitical scienceManagementLawComputer science

Abstract

fetched live from OpenAlex

Triple Helix theory prescribes coordinated actions among government, research institutions and industry to achieve growth. However, the Brazilian wine industry case shows that simply having the institutions is not enough – the institutional framework matters. This paper shows three problems in the Brazilian institutional design that hamper wine quality improvements and impede the development of an effective, fully-fledged Triple Helix model in the Brazilian wine industry. They are: overlapping jurisdictions between the federal and state governments; dissociation between the policy-making locus and the industry; and over-institutionalization. The latter cause is not well developed in the Triple Helix literature as yet. These three factors create coordination problems that appear to be almost insoluble in the present state, and underscore the need for designing institutions carefully before this impasse is reached.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.030
GPT teacher head0.223
Teacher spread0.193 · 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