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Record W2046879152 · doi:10.1068/a34211

Geographic Mobility and Residential Instability in Impoverished Rural Illinois Places

2005· article· en· W2046879152 on OpenAlexaff
Matt Foulkes, K. Bruce Newbold

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRentingGeographic mobilityGeographyPovertyCensusRural areaDemographic economicsLocationDistribution (mathematics)Economic growthSocioeconomicsEconomic geographySociologyDemographyEconomicsPolitical sciencePopulation

Abstract

fetched live from OpenAlex

Impoverished rural places are often depicted as immobile communities populated by less skilled, less educated nonmovers who have been left behind by selective out-migrants. Yet certain poor rural localities exhibit high rates of in-migration and residential mobility, an underresearched phenomenon not easily explained by conventional migration theory. The authors explore factors associated with high rates of geographic mobility in impoverished rural localities in Illinois. With the aid of place data from the 2000 Census, the authors test a hypothesized model of geographic mobility within rural impoverished Illinois places. In addition to factors commonly found in the residential mobility literature, such as age distribution, employment security, and life stage, the model also tests the effects of various indicators of housing costs and housing supply on geographic mobility rates in poor and nonpoor places. The results indicate that, after controlling for age structure and household type, accessible housing in the form of rental housing is strongly associated with high mobility rates, though the overall fit of the models is better for nonpoor places. These findings raise questions regarding whether geographic mobility in impoverished places behaves according to long-standing theory, and have implications for policies for tackling rural development, housing, and poverty issues.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.442

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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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

Citations13
Published2005
Admission routes1
Has abstractyes

Explore more

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