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Record W2280901089 · doi:10.1177/0308518x15609741

Tearing down the city to save it? ‘Back-door regionalism’ and the demolition coalition in Cleveland, Ohio

2015· article· en· W2280901089 on OpenAlexaff
Emily Rosenman, Samuel Walker

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsAusterityDemolitionPoliticsUrbanismPolitical economyPolitical scienceUrban politicsPublic administrationSociologyEngineeringCivil engineeringLawGeographyArchitectureArchaeology

Abstract

fetched live from OpenAlex

In this paper, we explore what Cleveland, Ohio’s program of demolishing abandoned and foreclosed houses can teach us about the logics and politics of post-2007 austerity urbanism. We investigate the emergence of a local political coalition that has promoted demolition as a solution to the city’s housing crisis. In tracking how this political consensus is spatialized, we analyze how demolition both supports and complicates existing theories of austerity urbanism. We theorize demolition as a spatio-temporal fix, a locally negotiated response to the larger-scale political-economic limits imposed by neoliberal austerity. This fix occurs at both the neighborhood level, where demolitions clear land for future reinvestment, and at the regional level, where the increasingly more-than-urban nature of the US housing crisis allows demolitions to gain regional support among fragmented municipalities. In the seemingly paradoxical pursuit of demolition as a growth strategy, political actors in cities like Cleveland aggressively push to tear down the city in a desperate attempt to save it.

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 score0.787
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.045
GPT teacher head0.254
Teacher spread0.208 · 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

Citations31
Published2015
Admission routes1
Has abstractyes

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