Changes in Canada’s Preferential Trade Network and the Welfare Effects in Agricultural Markets
Bibliographic record
Abstract
There have been some important changes in Canada’s preferential trade network over the last few years. At the regional level, the renegotiations over the NAFTA produced the generally similar USMCA. At the inter-regional level, the CETA and the CPTPP marked significant steps toward promoting Canada’s trade with distant countries. This article overviews the corresponding regional and inter-regional trade preferences for agricultural products. It examines the welfare effects of the USMCA and more pronounced regional preferential schemes, and those of the CETA and the CPTPP for Canada in the agricultural markets. It assesses the welfare outcomes from different scenarios involving various combinations of presence and absence of regional and inter-regional trade preferences. The analysis underlines that the deepening of North American market integration would lead to increases in Canada’s welfare. It shows that inter-regional trade preferences could exceed the USMCA/NAFTA in promoting imports in some cases, resulting in increases in Canada’s welfare. However, inter-regional trade preferences may not entirely substitute for the welfare losses resulting from the absence/elimination of regional trade preferences in some other cases. This article suggests that Canada would generally benefit from higher welfare levels across agricultural markets through a simultaneous network of regional and inter-regional trade preferences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.006 | 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".