The Wine Industry in British Columbia: A Closed Wine But Showing Potential
Bibliographic record
Abstract
The analysis in this report is structured around a theoretical framework I have developed for the larger comparative project- that industry competitiveness depends on policies that guide: markets, institutions, networks, and supply chains. We apply the framework to the British Columbia (BC) Canada wine industry, with an emphasis on the area with the greatest concentration, the Okanagan Valley (OKV). Our approach focuses on the potential role of public and collective support institutions to promote industry competitiveness in clusters. By clusters, we mean geographically concentrated producers in the same industry. I take an evolutionary view of the role of such institutions, reflecting my recent work that a successful public-private partnership requires continual adaptation to changes in markets (Hira, forthcoming). I therefore completely reject the false dichotomy that prevails that either markets (private companies) or states (governments) determine economic success. Productive public-private interactions are fundamental to successful industries. The analysis in this report strongly reinforces this point- to be successful BC needed and will need public-private partnerships that are responsive, flexible, and pro-active.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".