Bibliographic record
Abstract
The COVID-19 pandemic has left many international students and postdocs uncertain about their future. Caught on vacation or between jobs, they are unable to travel to their labs because of closed borders, canceled flights, and shuttered consular offices. And as restrictions continue, the fate of these trainees, their labs, and their departments are up in the air. The pandemic adds to concerns by US scientists that the country is no longer seen as a welcoming place for international researchers (see page 34). When he left Canada for India in February, Varoon Singh thought he was just heading home for a brief visit before starting a postdoctoral position at Ghent University in Belgium. Singh had just finished a postdoc at the University of Waterloo. He’d struggled in Canada to get a medical checkup required by Belgium, so he thought he would visit family in India and get his visa straightened out
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.091 | 0.032 |
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".