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Social polarization and the politics of low income mortgage lending in the united states

2003· article· en· W1987680733 on OpenAlexaff
Jason Hackworth, Elvin Wyly

Bibliographic record

VenueGeografiska Annaler Series B Human Geography · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
Fundersnot available
KeywordsPoliticsPolarization (electrochemistry)DisinvestmentInequalityEthnic groupEconomicsEconomic inequalityCapital (architecture)Political economyDemographic economicsSociologyPolitical scienceGeographyMarket economy

Abstract

fetched live from OpenAlex

The structured inequalities of capital investment and disinvestment are prominent themes in critical urban and regional research, but many accounts portray ‘capital’ as a global, faceless and placeless abstraction operating according to a hidden, unitary logic. Sweeping political‐economic shifts in the last generation demonstrate that capital may shape urban and regional processes in many different ways, and each of these manifestations creates distinct constraints and opportunities. In this paper, we analyze a new institutional configuration in the USA that is reshaping access to wealth among the poor – a policy ‘consensus’ to expand home‐ownership among long‐excluded populations. This shift has opened access to some low‐ and moderate‐income households, and racial and ethnic minorities, but the necessary corollary is a greater polarization between those who are able to own and those who are not. We provide a critical analysis of these changes, drawing on national housing finance statistics as well as a multivariate analysis of differences between owners and renters in the 1990s in New York City. As home‐ownership strengthens its role as a privatized form of stealth urban and housing policy in the USA, its continued expansion drives a corresponding reconstruction of its value for different groups, and inscribes a sharper axis of property‐rights inequalities among owners and renters in the working classes.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.225
Teacher spread0.206 · 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 teacher head, 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

Citations14
Published2003
Admission routes1
Has abstractyes

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