Bibliographic record
Abstract
Abstract The Canadian wine industry has a relatively short, yet successful, history. Beginning in the 1970s, two parallel policy tracks emerged: the first is a sector development one, and the second a retail and trade one. Together they fostered the development of the industry, especially at the domestic level, by modernizing the cultivation and wine-making processes and by supporting the increased demand for Canadian products both domestically and internationally. The sector, marked by a complex set of actors that are often in only partial policy alignment and have diverse goals, has developed through three temporal stages: Emergence (1970s–1995), Expansion (1996–2005), and Maturity (2006 onward) and, in the latter, the booming domestic wine consumption has drawn attention to a policy regime that—largely in the hands of provincial authorities—has been criticized for creating a series of impediments to trade and fair access to the provincial market. Policy shifts are being imposed by Canada’s international trade commitments and by the evolution of the industry itself—this will have important effects on the policy latitude of the actors and likely will rewrite some of the approaches that the provincial governments have been relying on for the past decades.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".