State Firearm Laws, Firearm Ownership, and Safety Practices Among Families of Preschool-Aged Children
Bibliographic record
Abstract
OBJECTIVES: We investigated how state-level firearms legislation is associated with firearm ownership and storage among families with preschool-aged children. METHODS: Using 2005 nationally representative data from the Early Childhood Longitudinal Study-Birth Cohort (n = 8100), we conducted multinomial regression models to examine the associations between state-level firearms legislation generally, child access prevention (CAP) firearms legislation specifically, and parental firearm ownership and storage safety practices. RESULTS: Overall, 8% of families with children aged 4 years living in states with stronger firearm laws and CAP laws owned firearms compared with 24% of families in states with weaker firearm laws and no CAP laws. Storage behaviors of firearm owners differed minimally across legislative contexts. When we controlled for family- and state-level characteristics, we found that firearm legislation and CAP laws interacted to predict ownership and storage behaviors, with unsafe storage least likely among families in states with both CAP laws and stronger firearm legislation. CONCLUSIONS: Broader firearm legislation is linked with the efficacy of child-specific legislation in promoting responsible firearm ownership.
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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 | 0.004 |
| 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 | 0.000 |
| 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".