Revisiting Trump’s Protectionism and Its Policy Implications for US–Canada Trade Relations
Bibliographic record
Abstract
Amid the rise of economic nationalism and increasing uncertainty in global trade governance, understanding the impact of unilateral trade enforcement on bilateral relations has become critically important. This study explores the resurgence of protectionist policies under President Donald J. Trump, with a particular focus on their effects on U.S.–Canada trade relations and the broader global trend toward economic nationalism. Employing a qualitative case study approach supported by a simple tariff simulation, the research examines how instruments such as anti-dumping (AD) and countervailing duties (CVD) were utilized to protect domestic industries. The simulation indicates that imposing a 21% tariff on Canadian softwood lumber could significantly reduce import volumes while potentially increasing domestic output by 121.8%. However, these protectionist measures also intensified trade tensions, disrupted longstanding alliances, and weakened trust in multilateral institutions such as the WTO. By integrating empirical estimation with policy narrative analysis, this study contributes to the literature on trade policy, emphasizing that while unilateral protectionism may offer short-term domestic advantages, it requires careful calibration with economic diplomacy to ensure the sustainability of global trade cooperation.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".