Diversion, and Racial Discrimination Against Asian Technology Professionals
Bibliographic record
Abstract
American business interests face increasing difficulties as they attempt to compete against global technology-based industries. As the U.S. educational system produces interests face increasing difficulties as they attempt to compete fewer technology workers, many firms look to foreign countries such as India, China, or other Asian countries that have an abundance of skilled professionals. The U.S. Congress created the H-1B visa program in 1990 for educated skilled foreign workers, and manipulated the yearly cap on several occasions. Limits were as high as 195,000 as recently as 2003, but were reduced to 65,000 by 2009. The result of placing a low cap on available H-1B visas places a hardship both on domestic high-technology businesses, which cannot get sufficient quantities of desired workers to fill employment slots, but to the U.S. as well with reduced opportunities to recruit potential educated citizens. An unintended consequence of fewer H-1B visas produces a reduction of overall potential national brain gain optimization that could result from the spillover and agglomeration effects from the exchange of ideas in the marketplace (an effect that I refer to as brain blocking). Further, the brain gain that could have been accrued to the U.S. has been re-routed, either to immigration-friendly countries such as Canada or remains in the Asian professional’s home country if the worker decided to stay there (an effect that I refer to as brain diversion). Further, the imposition of a low H-1B visa cap appears to have similarities to historical race-based immigration restrictions that kept Chinese and other Asian workers out of the domestic workforce in earlier centuries.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".