Navigating Tariffs through Trade Diplomacy: Strategies and Lessons from Select Countries
Bibliographic record
Abstract
This paper explores the vital role of trade diplomacy in navigating the global economic volatility spurred by the broad-based US tariffs imposed since January 2025, which were anchored on national security concerns. More specifically, it reviews the trade diplomacy tactics employed by Canada, Germany, Mexico, and Vietnam vis-à-vis the US tariffs. Using a qualitative, descriptive methodology, the findings reveal both similarities and differences in their approaches. On one hand, all countries deployed a multi-pronged strategy and are pursuing trade diversification efforts to de-risk from great powers such as the US and China. Further, initiatives are being intensified to promote domestic industry resilience while balancing political, economic, social, and geostrategic factors. On the other hand, advanced economies (Canada and Germany) adopted a "retaliate-to-negotiate" approach, matching US tariffs tit-for-tat and filing WTO complaints while emerging economies (Mexico, Vietnam) used a "concession-to-de-escalate" strategy, delaying retaliation and offering proactive concessions on security, non-tariff barriers, and major purchases to secure lower tariff rates. The study also finds that countries with existing FTAs (Canada, Mexico) were significantly shielded from the tariffs. This paper thus provides a timely analysis of key trade diplomacy strategies amid unilateral, reciprocal tariffs and outlines practical lessons for other economies operating in a fragmented and increasingly protectionist economic landscape.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".