L’impact d’une zone de libre-échange entre le Canada et les États-Unis : examen critique de l’étude de Wonnacott
Bibliographic record
Abstract
The Wonnacott study concludes that the most realistic and beneficial commercial policy Canada is likely to be able to pursue at the moment would be to negotiate a Canada-U.S. free trade arrangement. This paper consist primarily of assigning different weights to a variety of the considerations Wonnacott raises and analyzing the effects of these different weights on his conclusions regarding the appropriate free trade strategy for Canada. A number of issues are also raised which do not appear to have been examined in Wonnacott's analysis. The thrust of the paper is three-fold. First, there may not be as large a net economic benefits of Canada-United States free trade as the Wonnacott presentation suggests. Second, depending upon the weight one assigns to some of the political-economic factors, the advantages of the former policy may be reduced even more and accordingly we should not be stampeded into a bilateral Canada-United States agreement. And third, if any change in commercial policy is to have a maximum beneficial effect upon Canada, then regardless of what commercial policy is followed, more attention will have to be given to a number of broader issues such as Canadian practices and policies relating to its overall balance of payments (including capital inflows and their effects upon the Canadian dollar), and to protectionnist provincial policies encouraging the fragmentation of industry.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.021 | 0.031 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".