Bibliographic record
Abstract
This paper examines immigration and the wages of foreign and native nurses in the US labor market. Data from the Current Population Survey identifies a worker's country of birth and the National Survey of Registered Nurses (NSRN) identifies nurses who received their basic training outside the US. In 2004 about 3.1% of the registered nurse (RN) workforce is foreign-born non-US citizens, and 3.3% received their basic education elsewhere. The principal countries of origin are the Philippines, Canada, India, and England. Regression results show a 4.5% lower wage for non-citizen nurses born outside of the US (Canadian nurses are an exception). The wage disadvantage is concentrated on foreign-born nurses new to the US; once a nurse has been in the US for 6 years there is no longer a significant penalty. Results from the NSRN show relatively little overall wage differences between RNs who received their basic training outside versus inside the US, but there is a significant wage disadvantage for those new to the US market. The presence of foreign-trained nurses appears to decrease earnings for native RNs, but the effects are small.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".