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Record W3166382957 · doi:10.1177/23315024211035591

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

2021· article· en· W3166382957 on OpenAlexaboutno aff
Donald Kerwin, Robert Warren, Charles Wheeler

Bibliographic record

VenueJournal on Migration and Human Security · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsNaturalizationCitizenshipResidenceImmigrationLegislatureImmigration reformAdministration (probate law)Immigration lawPolitical scienceSociologyDemographic economicsPoliticsLawEconomicsDemography

Abstract

fetched live from OpenAlex

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.056
GPT teacher head0.377
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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