Concentration of Cannabis and Tobacco Retailers in Los Angeles County, California: A Spatial Analysis of Potential Effects on Youth and Ethnic Minorities
Bibliographic record
Abstract
Objective: Cannabis and tobacco retailers are believed to cluster in areas with more racial/ethnic minorities, which could account for the disproportionate use of blunts in Black and Hispanic communities. The current study examined the spatial relationship between cannabis and licensed tobacco retailers in Los Angeles County, California, and assessed whether various neighborhood and business factors influenced the spatial patterning. Method: Generalized additive models were used to test the association between the location of cannabis retailers (N = 429) and their accessibility potential (AP) to tobacco retailers (N = 8,033). The covariates included cannabis licensure status, median household income, population density, percentages of racial/ethnic minorities and young adults (18–34), unemployment status, families living in poverty, minimum completion of high school/General Educational Development (GED) credential, and industrial businesses by census tract. Results: The location of cannabis retailers was significantly associated with AP in all adjusted models (p < .005). The percentage of racial/ethnic minorities, age (18–34 years), and nonlicensure of cannabis retailers, which were positively correlated with AP (p < .05), confounded the association between AP and cannabis retailer location. Conclusions: The concentration of unlicensed cannabis retailers and tobacco retailers in young and racially/ethnically diverse neighborhoods may increase access to and use of cigarillos for blunt smoking. Jurisdictions within Los Angeles County should consider passing ordinances requiring minimum distances between cannabis and tobacco retailers.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".