Are winemaker consultants just another source of knowledge for innovation?
Bibliographic record
Abstract
Purpose This paper aims to explore the effects of multiple external sources of knowledge and of the use of winemaker consultants on innovation in the Canadian wine industry. Design/methodology/approach The data for the study are taken from an original survey of wine firms in Canada covering the 2007-2009 period. The survey was carried out by computer-assisted telephone interviews, and it was addressed to winery firms that are engaged in growing grapes and producing wine. Findings The results show that the use of winemaker consultants positively affects all forms of innovation. At the same, as far as external knowledge sources are concerned, marketing sources positively affect all types of innovation, while research sources and general sources have a positive influence on particular forms of innovation. The results also show that winemaker consultants interact with other knowledge sources. Nevertheless, there are important nuances with regard to which type of knowledge sources is more compatible with the use of winemaker consultants for which type of innovation. Originality/value To date, there is no empirical evidence of the extent to which the use of external winemaker consultants and external knowledge sources interact together and what are their impacts on the introduction of different forms of innovation.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".