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Record W4207029744 · doi:10.54825/nial2618

Toward a Politics of Accountability: Feminist ethics of care and whiteness in Detroit's foreclosure crisis

2019· article· en· W4207029744 on OpenAlexaff
Rachael Baker

Bibliographic record

VenueRadical Housing Journal · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsYork University
Fundersnot available
KeywordsForeclosureAccountabilityPoliticsHarmPolitical economySociologyUnderclassPolitical sciencePublic administrationCriminologyLaw

Abstract

fetched live from OpenAlex

In the decade since the 2008 mortgage crisis, residents of Detroit, Michigan have continued to sustain anemic levels of preventable foreclosures by tax delinquency. The city’s decades-long over assessment of property values and proceeding windfall of tax foreclosures are happening amid a post-bankruptcy governance regime to remarketize housing and land that has been accumulated by the city through forfeitures and seizures. Over 50% of the city’s households, rented or owned, are led by African American women. Growing economic inequality and community efforts to keep Detroit a majority black city have roused organized responses against territorial reconfigurations that could drive further political-economic division and displacement. The Tricycle Collective, a woman-led non-profit that assisted Detroit households in avoiding tax foreclosure, will be examined here for their use of a feminist ethics of care in their approach to foreclosure prevention. This article considers the potential for harm in exercising an ethics of care within a deeply racialized housing market, without the intention of constructing next steps for advocates and activists to direct opposition toward the ongoing crisis of racialized dispossession. Speaking through critical race studies, urban geography and feminist theory, a feminist ethics of care will be deconstructed alongside what I call a “politics of accountability”, as a framework for action and analysis.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.066
GPT teacher head0.418
Teacher spread0.353 · 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 designQualitative
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

Citations2
Published2019
Admission routes1
Has abstractyes

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