Making Citizenship an Organizing Principle of the US Immigration System: An Analysis of How and Why to Broaden Access to Permanent Residence and Naturalization for New Americans
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes that the United States treat naturalization not as the culmination of a long and uncertain individual process, but as an organizing principle of the US immigration system and its expectation for new Americans. It comes at a historic inflection point, following the chaotic departure of one of the most nativist administrations in US history and in the early months of a new administration whose executive orders, administrative actions, and legislative proposals augur a different view of immigrants and immigration. The paper examines two main ways that the Biden–Harris administration can realize its immigration, naturalization and integration goals: i.e., by expanding access to permanent residence and by increasing naturalization numbers and rates. First, it proposes administrative and, to a lesser degree, legislative measures that would expand the pool of eligible-to-naturalize immigrants. Second, it identifies three underlying factors—financial resources, English language proficiency, and education—that strongly influence naturalization rates. These factors must be addressed, in large part, outside of and prior to the naturalization process. In addition, it provides detailed estimates of populations with large eligible-to-naturalize numbers, populations that naturalize at low rates, and populations with increasing naturalization rates. It argues that the administration's immigration strategy should prioritize all three groups for naturalization. The paper endorses the provisions of the US Citizenship Act that would place undocumented and temporary residents on a path to permanent residence and citizenship, would reduce family- and employment-based visa backlogs, and would eliminate disincentives and barriers to permanent residence. It supports the Biden-Harris administration's early executive actions and proposes additional measures to increase access to permanent residence and naturalization. It also endorses and seeks to inform the administration's plan to improve and expedite the naturalization process and to promote naturalization. The paper's major findings regarding the eligible-to-naturalize population include the following: In 2019, about 74 percent, or 23.1 million, of the 31.2 million immigrants (that were eligible for naturalization) had naturalized. Three states—Indiana, Arizona, and Texas—had naturalization rates of 67 percent, well below the national average of 74 percent. Fresno, California had the lowest naturalization rate (58 percent) of the 25 metropolitan (metro) areas with the largest eligible-to-naturalize populations, followed by Phoenix at 66 percent and San Antonio and Austin at 67 percent. Four cities in California had rates of 52–58 percent—Salinas, Bakersfield, Fresno, and Santa Maria-Santa Barbara. McAllen, Laredo, and Brownsville had the lowest naturalization rates in Texas. Immigrants from Japan had the lowest naturalization rate (47 percent) by country of origin, followed by four countries in the 60–63 percent range—Mexico, Canada, Honduras, and the United Kingdom. Guatemala and El Salvador each had rates of 67 percent. Median household income was $25,800, or 27 percent, higher for the naturalized population, compared to the population that had not naturalized (after an average of 23 years in the United States for both groups). In the past 10 years, naturalization rates for China and India have fallen, and rates for Mexico and Central America have increased (keeping duration of residence constant). In short, the paper provides a roadmap of policy measures to expand the eligible-to-naturalize population, and the factors and populations that the Biden–Harris administration should prioritize to increase naturalization rates, as a prerequisite to the full integration and participation of immigrants, their families, and their descendants in the nation's life.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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