Comparing American State Resident Neuroticism and State Tightness-Looseness as Predictors of Annual State Residential Mobility
Bibliographic record
Abstract
State resident neuroticism and the Harrington and Gelfand state tightness-looseness dimension were compared as predictors of state levels of residential mobility from 2004 to 2005 in the 50 American states. Hierarchical multiple regression controlled for state SES, white population percent, urban population percent, home ownership percent, and percent of home owners or renters paying 30 percent or more of household income for housing. Not moving was associated with higher neuroticism but not with tightness-looseness. Same-county moving, different-county moving, and within-state moving was associated with lower neuroticism but tightness-looseness was unrelated to any of these three criteria. However, lower tightness was associated with different-state moving and higher tightness was associated with greater tendency to move within a state rather than to a different state. Neuroticism showed no relation to the ratio of different-state to same-state moving. Results suggest distance moved may determine when neuroticism or tightness-looseness is a residential mobility predictor.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".