Naturalization of S.F. Chinese Immigrants: The Surge in the 1990s
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».