Bibliographic record
Abstract
Most observers who attempt to understand and explain President Trumps approach toward U.S. foreign policy tend to focus on explanations that emphasize personal behavioral indicators, political calculations squarely focused on the core priorities of his conservative base, and his general lack of appetite for the complexities of foreign affairs. As a self-described businessman and dealmaker, his view of foreign policy tends to be more practical and leans toward a model that focuses on outcomes rather than on relationships and is more geared to short-term solutions. Yet, in the search for convincing explanations, what has too long been ignored as the principal force informing, shaping, and ultimately defining President Trumps general preferences, not to mention his specific policy choices, is the world in which the president finds himself. This chapter looks specifically at how changes at the level of the international system have come to affect U.S. foreign policy and have helped to shape the presidents strategy. Specifically, the authors examine President Trumps efforts to reconfigure Americas relationship with Canada. Since 2016, two areas in particular have been the target of the current administration and the presidents America First commitment: a desire to renegotiate and fix the North American Free Trade Agreement (NAFTA), and efforts to secure a more meaningful commitment from Ottawa to the North Atlantic Treaty Organization (NATO). This chapter demonstrates that the application of American power and influence in these specific cases yielded dividends, resulting in significant, albeit modest changes consistent with Washingtons interests.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".