Learning, Innovation And Cluster Growth: A Study of Two Inherited Organizations in the Niagara Peninsula Wine Cluster
Bibliographic record
Abstract
This paper applies an innovation system framework to analyze the development of a natural resource-based system of innovation within the wine cluster in the Niagara Peninsula in Canada. A variety of policies shape the parameters (financial, fiscal, legal) within which opportunities for innovation open or are constrained and choices are made. Two of these have led to inherited organizations that have created contradictory incentives for innovation and growth in the cluster. On the input side, it is often said that great wines are 'grown in the vineyard' and the demand for innovation in the grape sector, thus depends upon the relationship between clients, in this case, vintners and their suppliers of grapes. That relationship is a learned one and the interactions within the Ontario Grape Growers Marketing Board (OGGMB), now the Ontario Grape Growers (OGG) have had a powerful, and not always positive, impact on the innovation process. With regard to outputs, policies affecting the sale and distribution of wine as administered through the Liquor Control Board of Ontario have created a 'glass ceiling' that is a disincentive for growth and innovation among small wineries.
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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 | 0.002 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".