Bibliographic record
Abstract
Over one hundred thousand lives were lost in the United States during 2022 from drug overdoses (CDC, 2023). While previous studies examine socioeconomic and macroeconomic relationships, I have not found extensions to estimate a relationship between media influence and overdoses. I investigated the potential relationship by including a Google search trend index (a measure of relative state-level popularity of web searches) for trap music as a proxy for media influence of trap music. Trap music is a new mainstream subgenre of hip-hop which portrays a romanticized view of illicit substance sale and abuse. My logic is younger demographics are more likely to listen and be influenced by the subgenre, meaning high interest in the subgenre would be correlated with overdose rates for younger demographics. Employing a fixed effects model (and controlling for macroeconomic and socioeconomic variables) across 40 states from 2013-2023, I find a one standard deviation increase in Google search intensity (13.7 points) being associated with a 1.98 increase in drug overdose deaths per 100,000 for those aged 15-34 years, statistically significant at the 5 percent error level. Those aged 35-54 reflected no measurable relationship. Interestingly, I found no relationship between the number of opioid treatment program facilities and overdose. My findings suggest media trends are associated with overdose deaths in addition to socioeconomic and macroeconomic trends for young demographics.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".