Recent Immigration and Economic Outcomes in Rural America
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
International immigration to the United States ebbed and flowed during the twentieth century (Gibson and Lennon 1999).After peaking at 14.8% in 1890, the percentage of foreign-born residents in the United States fell to 4.7% in 1970, slowly rising to 7.9% in 1990.However, since then, the share of foreign-born accelerated to 12.5% in 2006 (U.S. Census Bureau, American Community Survey 2006).The recent surge in immigration has provoked a heated debate regarding whether immigration is "good" or "bad" for American communities and workers (Borjas, Freeman, and Katz 1997;Saiz 2003).Immigration to rural America has historically been much lower than that for urban areas, most likely due to beachhead effects and the likelihood of better job matches in urban areas.Using 2003 metropolitan area (MSA) definitions, 1990 Census of Population data suggest that about 9.3% of metropolitan population was foreign-born, while the nonmetropolitan share was only about 1.8% (U.S. Census Bureau, 1990 Census, 2008).By 2006, the American Community Survey indicates that these shares had respectively risen to about 15.4% and 4.9%.In relative terms the metro share increased by about 66%, and the nonmetropolitan share increased by 172%.Despite the relative increase in rural immigration, most related studies have focused on states or metropolitan areas (e.g., Card 2001; Borjas 2005).Another shortcoming of past research is that despite the public and media attention on recent immigrants, most regional studies consider all immigrants as a
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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) | 0.000 | 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