The Dark Web and cannabis use in the United States: Evidence from a big data research design
Bibliographic record
Abstract
BACKGROUND: Cannabis is one of the most commonly sold drugs on cryptomarkets. Because of the anonymity-granting functions of Tor, no study has traced the within-country effect of the Dark Web on cannabis consumption patterns. This article uses a big data research design to examine the association between revealed interest in the Dark Web and self-reported cannabis use within US states from 2011 when Silk Road launched to 2015 when Operation Onymous shuttered nine markets. METHODS: This study uses mixed effects ordinary least squared regressions to analyze U.S. state/year panel data, using robust standard errors to correct for heteroscedasticity. Marginal effect plots illustrate substantive effects. The dataset consists of state-level variables drawn from the Uniform Crime Report (UCR), the American Community Survey (ACS), the National Survey on Drug Use and Health, the Correlates of State Policy Project, and the Bureau of Justice Statistics Justice Expenditure and Employment Extracts. Data for the Dark Web interest measure are drawn from Google Trends. The proxy for Dark Web interest is an index of eight Dark Web related search queries. RESULTS: The regression analysis indicates that Dark Web interest in US states positively correlates with cannabis consumption rates overall and among older adults (26+), but not youth (12-17) or younger adults (18-25). Additionally, Dark Web interest is positively associated with more frequent cannabis usage rates (i.e. use in the past month, excluding first time use) both overall and among older adults, but not among youth or younger adults. Dark Web interest does not correlate with casual use (i.e. use in the last year, excluding use in the past month) for any age bracket. Interacting Dark Web interest with state-level legalization regimes indicates that the association between Dark Web interest and cannabis consumption in the past year is no different in medically legalized states and amplified in states with recreational legalization. Lastly, the Dark Web interest term does not correlate with first time cannabis either overall or for any age category. CONCLUSIONS: Interest in the Dark Web is associated with increased cannabis use in U.S. states from 2011-2015, but the effect is concentrated in states with more frequent cannabis users, older users, and in states with recreational legalization of cannabis.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".