Introduction: Transnational American Studies in the "Age of Trump"
Bibliographic record
Abstract
Introduction by issue editors. This issue represents our journal’s first appearance after the onset of what multiple Americanists have referred to, and not in overly positive fashion, as the “Age of Trump.” A central theme of Trump administration discourse is its strident defense of physical borders manifested in harshly exclusionary policies, most notably the administration’s abandoning of the existing DACA program, as well as fostering increased visibility of white nationalist groups and openly racist discourse. At the same time, the White House, led by the president, has distinguished itself by its nationalist attacks on international trade and multilateral diplomacy. As scholars of American Studies based outside the United States, we both feel a special responsibility to make use of our position to investigate and discuss the larger forces at play here. One thing that larger transnational approaches can help reveal is the complex interface between national identity, domestic politics, and state policy, especially in regard to international relations.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.013 |
| 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".