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Enregistrement W4414201285 · doi:10.5406/19364695.45.1.16

Advancing Immigrant Rights in Houston

2025· article· en· W4414201285 sur OpenAlexaboutno aff
Marilynn S. Johnson

Notice bibliographique

RevueJournal of American Ethnic History · 2025
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueMigration and Labor Dynamics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésImmigrationDeportationPopulationPoliticsLatin AmericansQuarter (Canadian coin)NaturalizationImmigration policy

Résumé

récupéré en direct d'OpenAlex

Eight days before Inauguration Day 2025, the New York Times ran an editorial warning of the dangers of mass deportation by comparing Houston, Texas, to Birmingham, Alabama. Houston owes much of its dynamic economy to its welcoming of immigrants, the editors argued, while Birmingham's raft of anti-immigration measures has hastened that city's decline. Although the contrast is valid, Els de Graauw and Shannon Gleeson's recent book, Advancing Immigrant Rights in Houston, offers a more complex and critical analysis of Houston's response to its immigrant communities.As Texas's largest city and the fourth largest in the United States, Houston has experienced rapid growth in its immigrant population since the 1980s. Today, 29 percent of its population is foreign born, speaking 145 different languages. Once largely Mexican, the city's immigrant population now includes sizeable communities from other parts of Latin America, Asia, and Africa. Non-Hispanic whites make up only about a quarter of the city's residents, while Latinos are approaching a majority. Immigrants have found work in both the skilled sectors of medicine and high tech as well as in lower-paid construction and service industries.As the authors explain, integrating these immigrants has been challenging given the city's political and social context. A mixed political scene, a weak nonprofit sector, and Houston's location in a very conservative, anti-immigrant state means that a diverse array of local actors must work together to achieve change through negotiation and compromise. Chapter 2 presents four case studies of immigrant rights initiatives: the establishment of the city's immigrant affairs office, relationships with federal immigration enforcement, the provision of legal and citizenship services, and workplace rights. In the first two cases, progress has been sporadic, rising and falling with the political winds. In the second two cases, though, Houstonians achieved notable change by arguing for efforts such as citizenship services and wage theft legislation as conducive to good business practices and economic growth.The campaign against wage theft is perhaps the book's most illuminating story. The effort began under Democratic Mayor Lee Brown, who launched an investigation of labor abuses on city-funded construction sites. Labor and community activists pushed to expand the campaign to sites across the city, but various enforcement efforts failed. Wage theft—particularly among undocumented workers—was a major problem, but few of these workers were willing to interact with police authorities. The situation became even more critical following Hurricane Ike in 2008, which required an infusion of immigrant workers to help rebuild housing across the city.After the Houston Interfaith Worker Justice Center released a scathing report documenting widespread wage theft abuses, the center teamed up with the SEIU and other unions, faith groups, and reform-minded business leaders to launch the Down with Wage Theft campaign. The authors highlight the strange bedfellows in this campaign, including Republican construction magnate Stan Marek, a devout Catholic who opposed wage theft as both immoral and unfair to honest businesspeople. After years of organizing and negotiating with Marek and other business interests, a Wage Theft ordinance became law in 2013. The ordinance established a process for reporting wage theft and created a database of offending companies that could lose their city contracts or their business licenses if they fail to pay assessed penalties. Although staffing and oversight have been inadequate, the authors are “cautiously optimistic” such coalition efforts can advance immigrant rights through “a slow and contentious process” (p. 81).Showing how change can occur in a purple city in a deep red state, Houston's experience may be instructive for advancing immigrant rights in our current conservative era. But as the authors also point out, even modest reforms like those in Houston have run headlong into right-wing anti-immigrant policy-making at the state level. Efforts to limit the Houston Police Department's participation in immigration enforcement, for example, lost steam with the passage of SB4, a 2017 Texas law allowing local police to inquire about the immigration status of people they detain and requiring local police to cooperate with federal immigration authorities—effectively banning sanctuary cities. Likewise, the Texas Regulatory Consistency Act, passed in 2023, strips the authority of (typically blue) cities and counties to enact local ordinances or rules that exceed or conflict with state-level codes. The law will likely undercut Houston's wage theft law.Such state efforts to stymie immigrant-friendly policies at the local level have been taken up by the Trump administration that is now targeting blue states and sanctuary cities for mass deportation and funding cuts. Such punitive measures may not be successful, but they will make the process of advancing immigrant rights locally even more slow and contentious.

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)0,001
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: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,797
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
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 Opus0,011
Tête enseignante GPT0,310
Écart entre enseignants0,300 · 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'étudeSans objet
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
Publié2025
Routes d'admission1
Résumé présentoui

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