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Mapping Public Housing: The Case of New York City

2010· article· en· W1967925125 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCity and Community · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsPovertyPublic housingSpatial mismatchSubsidySubsidized housingSpatial inequalityPolitical scienceDemographic economicsEconomicsEconomic growthInequalityLaw

Abstract

fetched live from OpenAlex

In American popular discourse and policy debates, “public housing” conjures images of “the projects”—dysfunctional neighborhood imprints of a discredited welfare state. Yet this image, so important in justifying deconcentration, is a dangerous caricature of the diverse places where low–income public housing residents live, and it ignores a much larger public housing program—the $100 billion–plus annual mortgage interest tax concessions to (mostly) wealthy homeowners. in this article, we measure three spatial aspects of assisted housing, poverty, and wealth in New York City. First, local indicators of spatial association document a contingent link between assistance and poverty: vouchers are not consistently associated with poverty deconcentration. Second, spatial regressions confirm this result after controlling for racial segregation and spatial autocorrelation. Third, factor analyses and cluster classifications reveal a rich, complex neighborhood topography of poverty, wealth, and housing subsidy that defies the simplistic stereotypes of policy and popular discourse.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.191
GPT teacher head0.322
Teacher spread0.132 · 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