Not an ‘iron pipeline’, but many capillaries: regulating passive transactions in Los Angeles' secondary, illegal gun market
Bibliographic record
Abstract
Objectives California has strict firearm-related laws and is exceptional in its regulation of firearms retailers. Though evidence suggests that these laws can reduce illegal access to guns, high levels of gun violence persist in Los Angeles (LA), California. This research seeks to describe the sources of guns accessed by active offenders in LA, California and reports offenders' motivations for obtaining guns. Setting Los Angeles County Jail (LACJ) system (four facilities). Methods Random sampling from a screened pool of eligible participants was used to conduct qualitative semistructured interviews with 140 incarcerated gun offenders in one of four (LACJ) facilities. Researchers collected data on firearm acquisition, experiences related to gun violence, and other topics, using a validated survey instrument. Grounded theory guided the collection and analysis of data. Results Respondents reported possession of 77 specific guns (79.2% handguns) collectively. Social networks facilitate access to illegal guns; the majority of interviewees acquired their illegal guns through a social connection (85.7%) versus an outside broker/unregulated retailer (8.5%). Most guns were obtained through illegal purchase (n=51) or gift (n=15). A quarter of gun purchasers report engaging in a passive transaction, or one initiated by another party. Passive gun buyers were motivated by concerns for personal safety and/or economic opportunity. Conclusions In LA's illegal gun market, where existing social relationships facilitate access to guns across a diffuse network, individuals, influenced by both fear and economic opportunity, have frequent opportunities to illegally possess firearms through passive transactions. Gun policies should better target and minimise these transactions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".