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Record W3037304133 · doi:10.1111/grow.12385

DUI and STI innovation modes in the Canadian wine industry: The geography of interaction modes

2020· article· en· W3037304133 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueGrowth and Change · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsMcGill UniversityHEC Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEconomic geographyMode (computer interface)Sample (material)Association (psychology)GeographyRegional sciencePsychologyComputer sciencePhysics

Abstract

fetched live from OpenAlex

Abstract This paper explores the geography of collaborations and interactions that are linked to DUI (Doing, Using, and Interacting) and STI (Scientific and Technologically based Innovation) innovation modes in the wine industry: that is, the geography of interaction modes. DUI and STI interaction modes are analysed by exploring their association with innovation and the extent to which this varies with geography. The results, based upon firm‐level data from a sample of 151 Canadian wineries, suggest that different types of innovation are connected to specific interaction modes. We show that the effects of each interaction mode are strongly dependent on whether the mode is deployed regionally or non‐regionally. In particular, the paper highlights marked differences between regional and non‐regional DUI and between regional and non‐regional STI interactions modes with respect to their association with innovation outcomes.

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 score0.086
Threshold uncertainty score0.915

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 communication0.0000.000
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 head0.065
GPT teacher head0.232
Teacher spread0.167 · 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