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Enregistrement W4296709506 · doi:10.1162/jinh_r_01853

<i>One Quarter of the Nation: Immigration and the Transformation of America</i> by Nancy Foner

2022· article· en· W4296709506 sur OpenAlexaboutno aff
John Iceland

Notice bibliographique

RevueThe Journal of Interdisciplinary History · 2022
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueMigration and Labor Dynamics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésImmigrationQuarter (Canadian coin)PopulationPoliticsPolitical scienceImmigration lawCensusImmigration policyChinaImmigration reformImmigration and crimeDevelopment economicsEconomic historyEconomyGeographySociologyHistoryLawDemographyEconomics

Résumé

récupéré en direct d'OpenAlex

Immigration is transforming America in a way that it had not for a century. The Immigration Restriction Act of 1921 put a ceiling on the overall number of immigrants allowed entry into the United States. The even tougher Immigration Act of 1924 followed, further diminishing the number of “undesirable” immigrants from Eastern and Southern Europe. By that time, immigration from China and Japan had also been severely limited by laws passed in 1882 and 1907. The passage of the Hart-Celler Act in 1965, however, marked a momentous reversal of these policies by re-opening immigration to a broader number of countries. Since then, immigration to the United States from around the globe has surged. Today, one-quarter of the U.S. population consists of either immigrants or children of immigrants, and they are remaking the country. Such is the story told in One Quarter of the Nation.Drawing data from various census surveys and a synthesis of historical and social-science research on immigration, Foner’s central thesis is that “post-1965 immigration has been a prominent source of profound and far-reaching changes in this country’s institutions, altering the social, economic, cultural, and political landscape in many significant ways” (4). Hence, the book is divided into chapters on race, changing cities and communities, the economy, culture, and electoral politics.The first chapter describes the effect of immigration on the racial order. In 1960, 85 percent of Americans were white; by 2018, the share had shrunk to 60 percent. Foner charts the rapid rise of the Asian and Hispanic populations and their well-being today. Increasing diversity has also raised awareness of racial identity among whites, as reflected in the current discourse on “whiteness.” Foner notes the challenges in predicting the future of the racial order, especially with growing levels of intermarriage among all groups. One plausible outcome, in line with Alba’s argument, is the eventual decline in the importance of race.1 Another possible prospect is that blacks will face greater exclusion than people of other racial groups, reflecting the legacy of slavery and generations of oppression. In any case, Foner persuasively argues that immigration has irrevocably changed the racial order and the overwhelming demographic and social dominance of the white population.The following chapters are also insightful and informative. The chapter about changing cities and communities highlights how immigrants have revitalized many places threatened with economic and demographic decline. Immigrants have deeply impacted the economy as key players in big tech as well as in the service industry at every level. Immigrants are making important contributions to popular culture, including theater, dance, food, film, and music.The chapter about electoral politics is the least persuasive. A central argument is that the presidency of Donald Trump made anti-immigrant policies a central feature of Republican Party support and induced less-educated whites and those biased against racial and ethnic minorities to move from the Democratic to the Republican Party. As a result, minority voters have become a more critical part of the Democratic Party electorate. There is some truth to this, as less-educated whites have shifted to the Republican party, and the growing number of minority voters overall provides key support for Democrats. Foner, however, could have done more to differentiate between opposition to all immigration and illegal immigration, a key distinction made by many voters. In addition, working-class voters of all races are, at least to some degree, changing their party alliances from the Democratic to Republican party. As Teixeira argued, “[S]ince 2012, nonwhite working class voters have shifted away from the Democrats by 18 margin points, with a particularly sharp shift in the last election and particularly among Hispanics.”2 This observation suggests that electoral realignments are being driven by more than just anti-immigrant and anti-minority animus, a point that Foner acknowledges but underplays.Any quibbles with the electoral-politics chapter aside, one of the book’s strengths is its strong command of history. Many contemporary public commentators on immigration are unaware that the issues and challenges that the United States faces today are similar to those of the past. For instance, worries about how immigration will change the character of communities or the racial and ethnic composition of the country have been evident since the country’s founding. Yet, Foner points out the ways in which the current context also differs from that of the past, such as the growing multiculturalism and support for bilingual education, which were not widespread a century ago.Overall, this well-written and highly accessible book is a valuable contribution to the scholarship on immigration. Its deep historical standpoint and its impressive synthesis of research on current patterns and trends provides an insightful analysis of how immigration is transforming America.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,306
Score d'incertitude au seuil0,379

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,009
Tête enseignante GPT0,246
Écart entre enseignants0,237 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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Même revueThe Journal of Interdisciplinary HistoryMême sujetMigration and Labor DynamicsTravaux en français237 207