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Record W2114683960 · doi:10.1300/j134v09n02_02

Welfare Neighborhoods: Anatomy of a Concept

2005· article· en· W2114683960 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Poverty · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsWelfarePovertyDiversity (politics)ImmigrationWelfare systemCluster (spacecraft)CensusService (business)PaymentSocial WelfareInequalityEconomic growthSociologyDemographic economicsPolitical scienceEconomic geographyGeographyEconomicsEconomyPopulationDemographyLaw

Abstract

fetched live from OpenAlex

ABSTRACT This paper conceptualizes welfare neighborhoods–places where welfare payments have deeply insinuated themselves into the local economy and survival strategies of the poor. Moving beyond Wilson's concept of concentrated poverty, I recognize the diversity and heterogeneity of impoverished neighborhoods, as well as more fully develop the relationship between welfare and place. I propose three welfare neighborhood types–the jobless ghetto, immigrant enclave and service-dependent ghetto–which are then explored using 2000 census data and a k-means cluster analysis. I identify and map the three sets of welfare neighborhoods in the two most populous urban jurisdictions in the United States, New York City and Los Angeles County. In the conclusion, I emphasize the pressing issue of federal welfare reform, of how its most recent phase further thrusts welfare neighborhoods into the unfamiliar role of being catalysts for job creation and personal transformation.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.767
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.316
Teacher spread0.297 · 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