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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.220 | 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 teacher head, 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".