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Record W2021495351 · doi:10.2307/1389786

Suburbanization and Home Ownership: The Spatial Assimilation Process in U.S. Metropolitan Areas

2000· article· en· W2021495351 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

VenueSociological Perspectives · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSuburbanizationSocioeconomic statusAcculturationMetropolitan areaEthnic groupGeographyDemographic economicsLogistic regressionLogitSocioeconomicsEconomic geographyDemographyImmigrationSociologyEconomicsEconometricsStatistics

Abstract

fetched live from OpenAlex

This article provides a detailed picture of spatial assimilation by simultaneously considering suburbanization and home ownership in order to model the complexity of residential patterns in modern society. The data are from the 1% Sample of the 1990 PUMS. Multinominal logit analyses were used to estimate the effects of socioeconomic level, acculturation characteristics, and race/ethnicity on the likelihood of householders being home owners or renters by housing locations. The results show that these factors affect the likelihood of householders living in suburbs for each tenure status in unique ways. Second, contrary to the spatial assimilation model, there is evidence that householders who are more acculturated and have more socioeconomic resources would rather be home owners in the central city than live in the suburbs as renters. Finally, the results also suggest extensive differences across racial groups in the effects of socioeconomic resources and acculturation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.317
Teacher spread0.286 · 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