the “Brain Blocking ” of Asian High-Tech Professionals
Bibliographic record
Abstract
U.S. immigration policy created the H1-B visa program in 1990 for educated skilled foreign workers, and has manipulated the cap on several occasions. Limits were as high as 195,000 as recently as 2003, but was reduced to 65,000 by 2009. The result of the lowering of the H1-B visa cap placed a hardship both on domestic high-technology businesses, who could not get sufficient quantities of desired workers to fill employment slots, but to the country as well with reduced opportunities to recruit potential educated citizens and unintentionally produces a reduction of overall national brain gain effects that result from the agglomeration effects of 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 to the home country if the high-tech worker decided to stay there (an effect that I refer to as “brain diversion”). The reasons for the reduction of the H1-B visa cap appear to parallel other immigration restrictions that kept Chinese and other Asian workers out of the domestic workforce in earlier centuries, and are often racially
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".