Geographic Mobility and Residential Instability in Impoverished Rural Illinois Places
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".