Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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) | 0.001 | 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 it