Understanding innovation in Canadian wine regions: an exploratory study
Bibliographic record
Abstract
Purpose Despite the importance of innovation in and the growth of the wine industry in recent years, empirical research devoted to innovation in this industry remains scarce. The purpose of this paper is to contribute to filling this gap by exploring innovation among Canadian wine firms. Design/methodology/approach The data used in this paper are drawn from an original firm-level survey conducted between April and July 2018 to study the business and innovation strategies of Canadian winery firms over the 2015–2017 period. Findings First, the study has identified four innovation modes which are distinct in terms of firms’ strategy, innovation activities, and knowledge sourcing and openness. The second finding is that these different innovation modes are associated with different innovation outputs. The third finding is that there here is a tendency for certain innovation modes to better reflect firms in some regions, although all innovation modes are represented to different degrees in each of the three wine regions. Originality/value Empirical research devoted to innovation in this industry remains scarce. This paper contributes to filling this gap by exploring innovation among Canadian wine firms. These firms deal with several challenges and opportunities arising from the production and transformation of cool-climate grapes that impact on business innovation approaches.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".