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Record W3109159239 · doi:10.1177/0042098020965976

Old, small and unwanted: Post-war housing and neighbourhood socioeconomic status

2020· article· en· W3109159239 on OpenAlexaff
Lyndsey Rolheiser

Bibliographic record

VenueUrban Studies · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSocioeconomic statusNeighbourhood (mathematics)ObsolescenceDemographic economicsMetropolitan areaGeographyWorld War IIEconomic geographyDemographyDevelopment economicsEconomic growthEconomicsSociologyBusiness

Abstract

fetched live from OpenAlex

Post-war neighbourhoods across the USA have declined in socioeconomic status over the past few decades. Over this same time period, the relative status of many of these neighbourhoods has dipped below that of older neighbourhoods. With the characteristics of post-war housing being arguably undesirable by current standards, extant literature claims the functional obsolescence of post-war housing is contributing to low and declining neighbourhood socioeconomic status. What remains unclear is whether the effect observed is due to housing age – post-war housing is vulnerable to physical depreciation given its age – or if there is a true post-war vintage effect influencing neighbourhood socioeconomic status beyond what age alone would predict. Using a panel model spanning 1990 to 2010, three main findings emerge. First, the presence of greater shares of post-war housing in neighbourhoods is associated with a small but significant decrease in neighbourhood status. Second, this effect varies across and within urban and suburban neighbourhoods. Third, there exists substantial heterogeneity in the effect across metropolitan areas that differ by housing supply growth and price. Together, these results imply that policymakers should consider the negative effects of functional obsolescence on top of the ills associated with ageing homes within certain spatial contexts.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.043
GPT teacher head0.217
Teacher spread0.174 · 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.

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

Citations6
Published2020
Admission routes1
Has abstractyes

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