Not an ‘iron pipeline’, but many capillaries: regulating passive transactions in Los Angeles' secondary, illegal gun market
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it