International STEM Graduate Students: A Key to Strengthening the American Economy and Building Competitiveness
Bibliographic record
Abstract
The United States (US) is renowned for offering world-class education to thousands of international students seeking advanced degrees in the STEM fields. However, the US is at risk of losing a significant portion of this talent due to limited visa options. While US funds and resources are invested in training international STEM graduate students, many students are compelled to leave the US for other countries with more favorable visa policies. This potential loss of talent is particularly concerning as China is poised to overtake the US in Research and Development (R&D) investment, while countries like Canada, the United Kingdom, and Australia, along with China, are seeking to attract foreign high-skilled STEM talents with their visa programs. STEM jobs comprise 48% of the 100 fastest growing jobs in the US while STEM industries such as the semiconductor sector already struggle to meet their growing demand for high-skilled workers. These demands can be alleviated by international STEM graduate students. In order for Congress to leverage this economic opportunity before losing American-trained students to other countries, we propose the following: i) Exempt international STEM graduate students from the visa requirement of proving their intent to leave the US after graduation, ii) Increase or circumvent the annual numerical employment green card cap for international STEM graduate students, iii) Extend the unemployment grace period for H-1B and OPT visa holders to allow sufficient time to find a new job.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".