Selective migration in American interstate residential flow: Similarity of Big Five personality factors among origin and destination state residents
Bibliographic record
Abstract
Two studies using the 48 contiguous American states tested the general hypothesis that the percent of the total number of movers leaving an origin state that migrates to a particular destination state is related to the degree of similarity in Big Five personality between the residents of the origin state and the destination state. Datasets for 2005–2006 and 2016–2017 were analyzed. The hypothesis was tested using Pearson correlation and multiple regression strategies without and with consideration of the following state-level statistical controls: socioeconomic status based on two economic and two educational variables, unemployment rate, White population percent, urban population percent, conservatism, and road distance between state capitals for the 48 states. A consistent pattern of support for the hypothesis was found for each of the Big Five personality dimensions—openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism—for both datasets without and with statistical controls. Results without statistical controls demonstrated that movers from states with residents higher on a Big Five personality dimension indeed are more likely to migrate to states with residents higher on that personality dimension, and that movers from states with residents lower on a personality dimension are more likely to migrate to states with residents lower on that personality dimension. Similar results were obtained with statistical controls but the relations for conscientiousness were in the supportive direction but not statistically significant. It is speculated that these state-level relations are grounded in parallel individual-level relations suggested by the theories of selective migration, homophily, similarity-attraction, and person-environment fit.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".