Trade creation and trade diversion in the Canada – United States Free Trade Agreement
Bibliographic record
Abstract
In this paper the changes in trade patterns introduced by the Canada‐United States Free Trade Agreement are examined. Variation in the extent of tariff liberalization under the agreement is used to identify the impact of tariff liberalization on the growth of trade both with member countries and non‐member countries. Data at the commodity level are used, and the results indicate that the Canada‐United States Free Trade Agreement had substantial trade creation effects, with little evidence of trade diversion. JEL Classification: F13, F14 Création de commerce et diversion de commerce dans l'Accord de libre‐échange Canada‐U.S. Ce mémoire examine les changements dans les patterns de commerce international engendrés par l'Accord de libre‐échange entre la Canada et les Etats‐Unis. La variation dans l'intensité de libéralisation tarifaire selon les secteurs dans l'Accord est utilisée pour identifier l'impact de la libéralisation tarifaire sur la croissance du commerce à la fois entre les pays membres et avec les pays non‐membres. A l'aide de données par produits, on montre que l'Accord a eu des effets substantiels de création de commerce mais qu'il n'y a pas lieu de croire qu'il y a eu beaucoup de diversion de commerce.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".