Bibliographic record
Abstract
Canada has a small, but vibrant winemaking industry. Since the US-Canada Free Trade Agreement (FTA), which went into effect in 1987, growers have shifted to vinifera grapes and modern winemaking techniques and there has been an explosion in the number of wineries, which number about 150 in the Niagara peninsula alone. However, their share of the Ontario wine market, which fell with free trade, has continued to decline. This paper delves into the reasons for the downward trend. Ontario provides a unique source of data on the wine market, since a single price for each wine is enforced by the Liquor Control Board of Ontario (LCBO). The paper analyses data downloaded from the LCBO’s website at the end of 2012 for all Ontario white table wines available for sale, and for wines from both “old world” (France and Italy) and “new world” (Argentina and Chile) wine producers. It is shown that after controlling for wine characteristics Ontario wines are higher priced than their competitors. This helps to explain why, despite improvements in the quality of Ontario wines, the share of imports in LCBO sales has risen. Certain wine varieties, in particular Chardonnay and Riesling, command higher prices, while in Vintages stores (but not in ordinary LCBO outlets), both the age of the wine and alcohol content have a significant positive effect on price. In terms of exports, Canada is miniscule on the world wine market, and it does not have a revealed comparative advantage in wine (except for ice wine). In addition to the disadvantage on input costs, Ontario wine production also suffers from an industry structure that limits the extent that most wineries can exploit economies of scale. A limit in the number of off-winery stores--included in the FTA and subsequently NAFTA to prevent further protection of Canadian wines--has led to an uneven playing field in which two firms, one now American owned, operate the vast majority of those stores. Other wineries are limited to selling through the LCBO or at the winery. Further development of Ontario’s wine industry is likely to require opening up wine retailing to allow all wineries to benefit equally. In order to avoid the strictures of NAFTA, this would have to mean opening up competition to a wider range of wine retailers able to sell both domestic and imported wines.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".