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Record W2437295328 · doi:10.1177/0308518x16654914

Demolition as urban policy in the American Rust Belt

2016· article· en· W2437295328 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

VenueEnvironment and Planning A Economy and Space · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDemolitionRust (programming language)Urban policyIntervention (counseling)Affordable housingEconomyPolitical scienceGeographyEconomic growthUrban planningEconomicsCivil engineeringEngineeringArchaeology

Abstract

fetched live from OpenAlex

Demolition has long been a component of urban policy in the United States and elsewhere. Until recently, however, demolition was seen as a mere component of a wider policy—e.g. the first step to build an affordable housing complex, or a revived commercial strip. Recently some have suggested that demolition can have stand-alone regenerative effects—that is, if blighted housing is demolished, surrounding markets and neighborhoods will heal and regenerate without further intervention. This article challenges this logic by examining neighborhoods in the American Rust Belt where ad hoc demolition has been the predominant urban policy in the past 40 years. In total, there are 269 neighborhoods in 49 cities that have lost more than 50% of their housing since 1970. In aggregate, these activities have led to more housing loss, and affected more land area than even the urban renewal period, yet have not led to market rebound or a decrease in social marginality.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.216

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)0.0000.000

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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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