Frequency of and responses to illegal activity related to commerce in firearms: findings from the Firearms Licensee Survey
Bibliographic record
Abstract
BACKGROUND: Firearms may be obtained illegally from federally-licensed dealers and pawnbrokers through surrogate (straw) purchases, undocumented purchases and theft. Some retailers knowingly make illegal sales. OBJECTIVE: To obtain information about the frequency of and risk factors for these events, and retailers' reactions to them, directly from licensed retailers. METHODS: Survey of a random sample of 1601 licensed dealers and pawnbrokers in 43 states who were believed to sell ≥50 firearms annually, conducted by mail during June-August 2011. RESULTS: The response rate was 36.9%, typical of establishment surveys using such methods. In the preceding year, 67.3% of respondents experienced attempted straw purchases; 42.4% experienced undocumented purchase attempts. For each event, 10% reported ≥1 occurrence/month. A quarter (25.6%) experienced firearm theft in the preceding 5 years. Pawnbroker status, sales volume, denied sales and sales of firearms that were subsequently traced by law enforcement were associated with all outcomes in multivariate analysis. Estimates of retailer involvement in illegal sales (median 3%, IQR 1-10%) were related in multivariate analysis to respondents' age and sex, and to denied sales. In a hypothetical case involving 50 illegal sales, respondents recommended prolonged incarceration (median 10 years, IQR 5-20 years) and a substantial fine (median $50 000, IQR $10 000-$250 000) for retailers and made similar recommendations for buyers. CONCLUSIONS: Attempts to acquire firearms illegally from licensed dealers and pawnbrokers are common. Characteristics associated with frequency of occurrence may facilitate prevention efforts. Licensed retailers consider selling and buying firearms illegally to be serious crimes.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".