Rethinking the Politics of the International Student Experience in the Age of Trump
Bibliographic record
Abstract
We are living in troubling and uncertain times. Xenophobia is on the rise as right-wing, authoritarian nationalism has witnessed significant electoral gains and the very ideals of democratic inclusiveness and international pluralism are under direct attack. With the election of Donald Trump as President of the United States, the country with the largest share of international students globally is increasingly becoming an unwelcoming place to study abroad. On January 27, 2017, Trump issued an executive order prohibiting entry of citizens from seven Muslim-majority countries(Iran, Iraq, Libya, Somalia, Sudan, Syria, and Yemen), and severely restricting the admission of refugees, into the United States. This initial attempt at a “Muslim travel ban” was subsequently blocked by the federal courts, yet the ongoing efforts of the current U.S. administration to discriminate against Muslim travelers at the border have had a chilling effect on international travel more generally
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.043 | 0.047 |
| Scholarly communication | 0.032 | 0.027 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.016 | 0.044 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".