Forum: Did “America First” Construct America Irrelevant?
Bibliographic record
Abstract
Did "America First" construct America irrelevant? Answering this question has been the subject of much debate in the popular press, the policy community, and scholarly circles. That asked, it is worth remembering that scholars and policymakers have long argued that one of the most enduring and important aspects of the US role in the world is American structural power. Perhaps nowhere has the Trump administration's approach to world affairs been more notable in perhaps diminishing US structural power than in withdrawing from multilateral forums. On an individual level of analysis, however, Trump's ever-changing, whiplash style of leadership made allies and adversaries less certain about American actions, intentions, and the direction of future policy trajectories. These issues point to the possibility that such impacts were more about Trump being Trump and less about a decline in American structural power. Only time will tell whether President Biden is able to rebuild from that structural wreckage. With these ideas in mind, the forum editors asked scholars representing diverse voices and perspectives to provide varying analyses of America First, specifically in light of the emergence of multiple global challenges from the COVID-19 pandemic to racial reckoning through the climate crisis and more. As you will see, each author brings a decidedly different lens to the questions we pose below and also generates divergent analyses of the present and the future.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".