American state gun law strength and state resident differences in neuroticism levels
Bibliographic record
Abstract
Relations between state gun law strength and state-aggregated levels of Republican leaning, gun ownership, and resident Big Five neuroticism (based on 619,397 residents nationally) were determined in a state-level analysis of the 50 American states using multiple regression strategies with state socioeconomic status, white population percent, and urban population percent statistically controlled. In a standard hierarchical model with state gun law strength as the criterion, the three demographic variables accounted for 44.4% of the variance and the Big Five accounted for another 21.9%. When the Big Five entered stepwise after the demographics, neuroticism was the sole significant personality predictor, accounting for another 13.4% of the variance. Greater state gun law strength was associated with higher state resident neuroticism. Further hierarchical regression analyses showed that state Republican leaning and gun ownership could account separately and jointly for significant variance in state gun law strength but not with state resident neuroticism controlled.
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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 | 0.000 |
| 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) | 0.002 | 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".