Impact of the COVID-19 Pandemic and Inflation on the USCanada Seafood Trade
Bibliographic record
Abstract
Purpose – Canada is the top destination for seafood exports from the United States, and its second-largest import source. Conversely, the U.S. is the main export market for Canadian seafood, accounting for over 60% of its exports. This study investigates the impacts of the COVID-19 pandemic and inflation on bilateral seafood trade, analyzing export-import trends, comparative advantage, and trade competitiveness from 2000 to 2024. Design/Methodology/Approach – The study employs the Revealed Symmetric Comparative Advantage(RSCA) index to assess U.S. comparative advantage in seafood exports and the Vollrath Index to evaluate trade competitiveness with Canada. To quantify the impact of the COVID-19 pandemic and inflation on trade between the U.S. and Canada, we compared exports and imports during the pandemic and inflation period with normal years. Findings – Despite temporary fluctuations, the analysis revealed a long-term upward trend in seafood trade between the two countries. The trade relationship is defined by interdependence, complementary strengths, and mutual benefit. Although COVID-19 and inflation adversely affected trade flows, the U.S. retained its comparative advantage and trade competitiveness in seafood exports to Canada. Research Implications – The U.S.-Canada seafood trade showcases the value of comparative advantage and collaborative trade practices in achieving mutually beneficial outcomes. Strengthening this trade relationship requires leveraging consumer trust, integrated supply chains, and policy support. Strategic investments by U.S. federal and state governments in aquaculture and fisheries and efforts to expand market access and improve export competitiveness will be essential for sustaining and growing this vital trade partnership in the evolving global seafood economy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".