Residential Stability, Neighborhood Racial Composition, and the Subjective Assessment of Neighborhood Problems Among Older Adults
Why this work is in the frame
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Bibliographic record
Abstract
AbstractThis study investigates the effects of neighborhood racial composition and residential stability—as measured by the percentage of individuals who have lived in the same location for the past five years—on perceived neighborhood problems. Among a sample of older black and white adults, findings indicate that the patterns are contingent upon residents' race. For whites who reside in neighborhoods with a low percentage of black residents, greater residential stability is associated with fewer perceived neighborhood problems net of individual- and neighborhood-level disadvantage. For blacks, greater residential stability is associated with fewer neighborhood problems, but the percentage of black residents is associated with more neighborhood problems. In both cases, individual- and neighborhood-level socioeconomic disadvantages contribute to those patterns. These findings have implications for theories about the personal and social effects of residential stability and neighborhood racial composition, as well as race differences in the links between neighborhood context and the subjective assessment of neighborhood problems. NOTESNotes1 Some measures of the perceived risk of personal victimization ask questions that approximate measures of perceived neighborhood problems. For example, questions have asked individuals "How likely is it that you will have your car stolen; have someone break into your house; be robbed or mugged on the street; raped or sexually assaulted; murdered?" It is plausible that perceptions about the presence of neighborhood problems are conceptually similar to perceptions of personal victimization. Thus, in some respects, the measurement of perceived neighborhood problems may conceptually lie between the more extreme forms of personal victimization and the more global measures of neighborhood satisfaction and/or neighborhood desirability.2 In separate analyses, the residential stability × neighborhood disadvantage interaction term is not statistically significant in models that include and exclude the percentage of black residents. Additional tests for percent black × neighborhood disadvantage and residential stability × percent black × neighborhood disadvantage did not yield significant coefficients. For the sake of space, I do not include those results in Table 2 (additional analyses are available upon request).3 To assess that the racial differences in the observed patterns of Tables 2 and 3 do not occur by chance, I tested three-way interactions prior to disaggregating by race. Those results support the conclusion that the observed patterns differ significantly for blacks and whites. I report results separately by race for ease of presentation and interpretation. In addition, some readers may wonder about possible interactions among variables that are not part of the focal associations. I tested potential interactions between both residential stability and racial composition with age, home ownership, and residential tenure; none of those were statistically significant.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it