The US Eligible-to-Naturalize Population: Detailed Social and Economic Characteristics
Why this work is in the frame
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Bibliographic record
Abstract
Naturalization has long been recognized as a crucial step in the full integration of immigrants into US society. Yet until now, sufficient information on the naturalization-eligible has not been available that would allow the federal government, states, localities, and non-governmental service providers to develop targeted strategies on a local level to assist this population to naturalize and to overcome barriers to eligibility. This paper remedies that deficiency by providing detailed estimates on the naturalization-eligible from data collected in the US Census Bureau's American Community Survey (ACS). Naturalization rates have traditionally been calculated by dividing the naturalized or the “naturalization eligible” populations by all foreign-born persons; i.e., the naturalized, legal non-citizens, and undocumented residents. By including the unauthorized in this calculation, naturalization rates have appeared misleadingly low for populations that can naturalize. By contrast, the Center for Migration Studies of New York (CMS) provides “naturalization eligibility” rates, which it calculates by dividing the “naturalization eligible” by the foreign-born population, minus undocumented residents and legal residents who arrived after mid-2008. The paper reports that 8.6 million US residents were eligible to naturalize in 2013. This figure approximates the 8.8 million estimate of the US Department of Homeland Security (DHS). Mexican nationals constitute the largest naturalization-eligible population at 2.7 million, followed by Indian (337,000), Chinese (320,000), Cuban (316,000), and Canadian (313,000) nationals. Fifty countries have 25,000 or more naturalization-eligible persons. The large number of legally resident Mexican nationals and this population's high naturalization eligibility rate mean that US states with large Mexican populations have relatively high percentages of legal foreign-born residents who can naturalize. The overall “naturalization eligibility” rate was 31 percent in 2013, including 48 percent for Mexican nationals. Nine of the 25 largest US naturalization-eligible populations by source country have naturalization eligibility rates in excess of 40 percent, including Mexico (48 percent), Canada (45 percent), El Salvador (42 percent), the United Kingdom (41 percent), Guatemala (44 percent), Japan (56 percent), Honduras (48 percent), and Brazil (41 percent). On a state level, California, Texas, New York, and Florida contain roughly five million of the US naturalization-eligible or about 58 percent of the total population. The paper finds that a large number of naturalization-eligible immigrants may have difficulty meeting the naturalization requirements or may need intensive support to do so. This population likely includes substantial percentages of the 2.87 million naturalization-eligible who have lived in the United States for more than 25 years; 1.16 million who do not speak English; 3.0 million with less than a high school education; and the 1.8 million with incomes below the poverty level. On the other hand, high percentages of eligible immigrants would seem to be well-situated to naturalize, including those who have lived in the United States for more than 10 years (78 percent); are age 35 or older (74 percent); are married (64 percent); speak English well, very well, or only English (65 percent); have access to both a computer and the internet (74 percent); earn income above the poverty level (79 percent); and have health insurance (72 percent).
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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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