A new president in the White House: <b>i</b>mplications for Canadian agricultural trade
Bibliographic record
Abstract
Abstract Canadian agricultural trade has experienced several volatile periods over the past 15 years. The Great Recession (2007–2009), the 2015–2016 global trade slowdown, unilateral policy actions by the United States against key trade allies and the multilateral system more generally, and the impacts of the Covid‐19 pandemic are among the most significant events during this period. Given the close integration of Canadian and US agricultural markets, the recent US election is likely to again impact the relative competitiveness of Canadian agricultural exports. While many observers suggest President‐elect Joe Biden will return to normal times regarding multilateral cooperation with key allies and international institutions such as the World Trade Organization, the new administration is likely to face headwinds given the significant fraying of ties with key trading partners and allies due to disruptive actions taken by his predecessor. This article provides an overview of potential implications of a Biden administration for Canada's agricultural trade. We start by reviewing recent trade shock events affecting Canada's agricultural trade with a particular focus on trade actions taken by the United States. Relevant components of the President‐elect Biden's platform, considerations affecting the implementation of this platform, and the implications of this for Canadian agricultural trade are considered.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.054 | 0.009 |
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".