Role of international politics on agri‐food trade: Evidence from US–Canada bilateral relations
Bibliographic record
Abstract
Abstract A well‐functioning trade relationship between Canada and the United States is crucial to the economic vitality of the Canadian agri‐food industry. However, agri‐food trade is more susceptible than other sectors to political interventions. The Trump presidency has strained Canada–US relations and his trade policy actions have significantly increased trade restrictions and trade policy uncertainty and undermined the rules‐based global trading system. We examine the pattern of agri‐food trade between the two countries and find that the upward trajectory of bilateral agri‐food trade ended in 2013. Although this flatlining predates the Trump administration, we show that Trump increased trade policy uncertainty starting in 2017 and likely impacted further expansion of trade. We examine what might change under the Biden presidency and argue that the new administration is likely to restore strong relationships with allies and work to rebuild important international institutions such as the World Trade Organization (WTO). Although protectionist forces will continue to impact bilateral agri‐food trade, we expect closer political ties between a Biden administration and the Canadian Prime Minister. This should have a positive effect on the Canadian agri‐food industry by reducing trade uncertainties, thereby increasing agri‐food trade between Canada and the United States.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".