Naturalization of S.F. Chinese Immigrants: The Surge in the 1990s
Bibliographic record
Abstract
For the first time in decades, the 1990s experienced a significant increase in naturalization. The number of naturalized U.S. citizens rose from 6.5 to 11 million citizens by 2002 (Figure 1; Fix, Passel and Sucher, 2003). Chinese Americans in particular experienced a surge in naturalization during the 1990s. From 2002-2004, California had the largest percentage of naturalized persons. Immigrants from Asian countries comprised a significant portion of the total naturalized between 2002 and 2004. Additionally, the San Francisco metropolitan area had the tenth largest naturalization rate from 2002 to 2004 (Rytina and Saeger, 2005). At the same time, legislation and political events targeted immigrants in California, creating a negative political climate for foreign-born residents. This study examines naturalization rates among the Chinese-American population living in San Francisco during 1980-2000. This study compares the characteristics of Chinese immigrants in San Francisco who became citizens and those who have yet to become naturalized during this large wave of immigration and subsequent anti-immigrant legislation and sentiment. Previous research has examined Chinese immigrants on a larger scale; however, this study examines a smaller sample to account for differences associated with a specific region. Naturalization is the citizenship mechanism that grants immigrants constitutional rights and benefits as well as political participation in US society. Legal permanent US residents who are 18 years of age or older and have lived in the country for at least five years are eligible to gain citizenship (Rytina and Saeger, 2005). Becoming a citizen is the first benchmark toward immigrant civic participation. The rate of naturalization among Asian Pacific Americans determines the size of the APA population eligible to vote and also its political future as a voting base. In order to study immigrant Chinese voting behavior, it is necessary to first study the likelihood of naturalization among this group. Measuring naturalization rates over time and collecting demographic data aids in tracking the variation in the immigrant voter pool. A regression model is applied to study the influence of certain immigrant characteristics that influence the decision to naturalize. Characteristics such as age, sex, educational attainment, years of residence in the US, English language ability and place of birth have been thought to be strong indicators of naturalization rates among foreign-born populations (Ong and Nakanishi, 1996).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".