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Record W2297331841

'When the Black Bourgeoisie Meets the Truly Disadvantaged': Intra-Racial Politics of Class and Residential Choice in Prince George's County, MD

2014· article· en· W2297331841 on OpenAlexaboutno aff
Carley Michelle Shinault

Bibliographic record

VenueSSRN Electronic Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGentrificationPopulationRedevelopmentPublic housingPoliticsAmerican Community SurveySocioeconomic statusDisadvantagedSuburbanizationEconomic growthGeographyPolitical scienceSociologyEconomicsDemographyLawCensus
DOInot available

Abstract

fetched live from OpenAlex

Urban centers throughout the country are experiencing rapid transformation as affluent residents migrate from other areas or return from the suburbs. In the District of Columbia, the energy of urban renewal is palpable, with the sites and sounds of demolition and construction on almost every street. The landscape is not the only thing transforming in the capital once dubbed “Chocolate City”; the racial and socioeconomic composition has rapidly shifted as well. According to the New York Times, since 2000 Washington’s white population jumped by 31 percent, while the black population declined by 11 percent. In 2011, the first American city to have an African American majority lost its status after over half a century.Given the almost certain prospects for greater tax base to fill city coffers, local officials often appear eager to meet the demands of gentrifiers — frequently at the expense of long-time residents. With land being among the most in-demand commodities, Washington’s housing authority (DCHA), for example, has demolished over one thousand public housing units in the past decade. Modeled after the federal Hope VI program, the city’s New Communities Initiative targets public housing properties that are considered distressed and demolishes them with the promise of quick renovation and redevelopment into mixed-income communities. In all cases, public housing units are not replaced at an equal rate and often only one-third of residents are able to return. Because only a fraction of public housing residents can return after redevelopment, initiatives for mixed-income housing developments are supplemented with residential mobility programs. Bolstered by evidence from Chicago’s Gautreaux program and HUD’s Moving to Opportunity demonstration, housing authorities contend that blending different economic groups provides economic, political, and social benefits for the poor. Residents are therefore offered housing choice vouchers and encouraged to seek out homes in low-poverty communities. Among the target destinations are neighborhoods largely comprised of black middle and working class residents. For Washington, DC residents, Prince George’s County, MD (PG County) has been an early target for the displaced.This research explores social and political impacts of urban gentrification and class tensions within the black community. It relies on U.S. Census tract data and a multi-neighborhood sample of black middle class residents of PG County to examine resident attitudes and responses to the influx of low-income and public housing residents in their communities. Drawing on three levels of vantage points (individual residents, county public housing authorities, and national policy regulators), this study explores how race and class shape residential decisions and their impact on mixed-income community initiatives. Using nested logit modeling, this analysis demonstrates the strength of the relationship between class position and three forms of residential choice: exit, voice, and loyalty. The analysis also considers the effect of several moderators that impact the strength of the relationship (gender, family status, political affiliation, and length of time in community). Findings have implications for ongoing debates on the declining significance of race/increasing significance of class, social benefits of mixed-income communities, and the effects of urban gentrification on suburban neighborhoods.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.190
Threshold uncertainty score0.377

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0380.013
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.009
GPT teacher head0.276
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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