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Enregistrement W7058132607

Map 10. Elementary School Racial Composition by Black Population Living in Census Block Groups, Richmond-Henrico, Virginia, 1990, 2000, and 2010.

2016· article· en· W7058132607 sur OpenAlexaboutno aff

Notice bibliographique

RevueVCU Scholars Compass (Virginia Commonwealth University) · 2016
Typearticle
Langueen
DomainePhysics and Astronomy
ThématiqueMagnetic confinement fusion research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCensusPopulationRacial compositionWhite (mutation)Race (biology)Boundary (topology)Quarter (Canadian coin)Racial differences
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Map 10 illustrates the process of racial change and increasing segregation in an inner- ring suburban area just across Richmond’s northeastern border. In 1990, two small sections of central Henrico were less than 30 percent black, holdovers from a not-so-distant era when white residents accounted for more than 90 percent of the suburb’s population. More predominant, however, were diverse neighborhoods in which black residents made up between 30 and 70 percent of the population. At the same time, black residents accounted for the overwhelming share of residents in a handful of central Henrico neighborhoods, particularly those closest to the city-suburban line, almost mirroring patterns found in the city of Richmond. And in conjunction with those neighborhood trends, in 1990 elementary schools serving this portion of Henrico County enrolled large majorities of black students, though several still reported sizable shares of white students. A decade later, in 2000, larger portions of central Henrico were characterized by growing black isolation. Elementary schools in the area reflected these shifts, nearly all of which were intensely segregated by this point. By 2010, black population the movement of blacks across Richmond and Henrico’s school district boundary line meant that serious resegregation had taken root in the central portion of the suburb. Black students constituted nearly 100 percent of the student population in central Henrico’s elementary schools, almost certainly foreshadowing further neighborhood transition to come. Map 10 also shows that Latino students, for the first time, accounted for a small share of students in central Henrico—indicative of future patterns of black-Latino segregation. In another ten years, unless active steps are taken to provide black suburban students and residents with greater access to other parts of Henrico, large swaths of the county may look like a lot like the city’s historically segregated neighborhoods. Conversely, in some limited places Richmond is beginning to resemble the Henrico County of yesteryear. A growing portion of downtown and eastern neighborhoods reported majority white populations in 2010. That shift likely reflects the leading edge of Richmond’s first white population increase in decades, a trend that has intensified since the close of this study. When it comes to schools in and around those gentrifying city neighborhoods, however, little easing of extreme minority segregation is apparent—to date. The Richmond area showcases how significant demographic changes play out in a fragmented context that has chosen to ignore school (and neighborhood) segregation for nearly three decades. While the fragmentation of Richmond differs from the other three city-suburban school systems, it is emblematic of many other parts of the country. Rapid growth and demographic change in most metropolitan communities has largely occurred largely without accompanying policies seeking to harness the potential of those transformations. Instead, in many ways, law and policy have cemented tremendous inequities into metropolitan spaces. Source: U.S. Census, 2014, TigerLine Shapefiles.

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 candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,242
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0160,000

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 Opus0,008
Tête enseignante GPT0,228
Écart entre enseignants0,220 · 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.

Devis d'étudeObservationnel
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
Publié2016
Routes d'admission1
Résumé présentoui

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