Managing Protectionism: The Dairy Industry as a Source of Conflict between Québec and the United States
Bibliographic record
Abstract
Canada’s supply management system in dairy has long been a source of friction with the United States, particularly involving Québec, which produces nearly half of Canada’s milk. While most sectors were liberalized under the North American Free Trade Agreement (NAFTA) and its successor, the United States– Mexico-Canada Agreement (USMCA), dairy remains protected by quotas, tariffs, and price controls. The exclusion of American producers—especially in key electoral states such as Wisconsin and Michigan—has made Canadian dairy a repeated target of U.S. presidents. This article examines why supply management persists despite its economic costs. It situates dairy protection in Québec’s provincial identity, language politics, and rural traditions, showing how symbolic politics can outweigh efficiency arguments in a post-material society. It also draws upon the political science theory of entrenchment, which highlights how incumbent actors and interest groups in democratic states use institutional, legal, and strategic tools to resist legislative reform and preserve their advantages. Comparisons with New Zealand, Australia, and the European Union highlight that reform is possible, but also politically costly. The article also contrasts Québec’s defense of dairy with its embrace of liberalized trade in aluminum, steel, aircraft, softwood lumber, and critical and strategic minerals, illustrating the province’s dual international strategies. Finally, it assesses the stakes for the 2026 USMCA review, where U.S. negotiators are likely to press for expanded access to the Canadian dairy market while Québec pushes Ottawa to resist.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".