The Economic Case For Welcoming Immigrant Entrepreneurs
Bibliographic record
Abstract
There's something inherently entrepreneurial about leaving your home to start a new life in another country. Perhaps that is why immigrants tend to start businesses at a disproportionately higher rate than native-born Americans. In fact, more than 40 percent of the Fortune 500 companies in 2010 were founded by an immigrant or the child of an immigrant. Yet, despite their vast economic contributions, U.S. law provides no dedicated means for immigrant entrepreneurs to launch innovative companies in the United States.Meanwhile, other countries are stepping up to attract foreign entrepreneurs. With new visas, countries like Canada and New Zealand are competitors for international entrepreneurial talent. This policy brief suggests a visa for entrepreneurial immigrants could boost U.S. economic growth and create American jobs. Commonly called a startup visa, this new means of entry would allow immigrant entrepreneurs to start businesses in the United States after satisfying certain funding, employment, or other requirements.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.027 | 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".