Modes of innovation in the Canadian wine industry
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the sectoral variety and common patterns of innovation in the wine industry. It intends to explore the nature, extent and sources of variety of innovation in the Canadian wineries. Design/methodology/approach The data employed come from a firm‐level survey addressed to 146 wine establishments in Canada. Results were analysed using factor analysis and non‐parametric statistical analysis. Findings The results reveal wineries tend to introduce many innovation activities which are internalised or externalised, draw on a variety of different sources of information, with a clear distinction between market sources, government sources (laboratories, research centres) and educational establishments, and introduced different types of innovation, including product and process but also organisational innovation. Practical implications The results suggest individual wineries innovate differently, but within a limited number of fairly consistent modes. Originality/value There is presently no published research investigating the different modes of innovation with regards to the wine industry and the case of Canada can provide valuable insights to understand how innovation is developed and sustained in cool climate regions.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".