TRADE AND INTERNATIONAL POLICY Uneasy Birth: What Canadians Should Expect from a Canada-EU Trade Deal
Bibliographic record
Abstract
The proposed Canada-EU trade agreement is having a difficult birth. While Canadians wait for the conclusion of the negotiations, it is important to recall the key issues at stake. An ambitious trade deal with Europe would improve Canadians firms ’ access and ability to compete in the EU market, which is 10 times the size of Canada’s. It would enhance and diversify Canada’s opportunities for trade and, hence, for expanding Canadians ’ incomes. But many fear that Canada would make excessive “concessions ” in relation to these benefits, in particular regarding Canadian governments ’ room to maneuver on key policy issues. After reviewing the likely key components of any deal, which include market access, regulatory issues, intellectual property rights, public-sector procurement, and investment issues, the author concludes that fears of potentially negative effects are greatly exaggerated. If anything, the danger is that the agreement would not open markets enough. The completion of a comprehensive economic and trade agreement (CETA) between Canada and the European Union (EU), under negotiation since 2009, reportedly has been close at hand for some months now. If and when a deal is reached, Canadians will be able to pore over the detailed text as they debate whether it is in Canada’s interest to ratify it. Many of the key issues that would
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.020 |
| Scholarly communication | 0.032 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.025 | 0.018 |
| 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".