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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".