The effect of a police crackdown on online drug sales: on the importance of targeting vendors’ reputation?
Bibliographic record
Abstract
• This article evaluates the impact of a police crackdown on cannabis sales on anonymous drug markets using the synthetic difference-in-difference approach. • The police crackdown was associated with a decrease in cannabis sales and the number of vendors with an active listing on anonymous drug markets. • Results suggest that, to be effective, police operations should conduct activities (e.g., seizing cannabis parcels) likely to negatively affect vendors’ reputation. This article evaluates the effect of a police crackdown on cannabis vendors operating in cryptomarkets. The police seized >100 cannabis parcels in transit and arrested one runner during this crackdown. An automated web crawler was used to download, and structure content found on 30 cryptomarkets between 1 January 2015 and 26 June 2017. Data were then aggregated on a weekly basis. The synthetic difference-and-difference (SDiD) approach and its extension for event-study analysis were used to estimate the effect of the police crackdown on the number of cannabis transactions and vendors. Estimates from the SDiD models suggest that the crackdown was associated with a significant decline in cannabis transactions. The event-study analysis indicates that the effect was delayed and for a short time. Estimates from the SDiD show that the overall effect of the crackdown on vendors was not significant. The event-study analysis shows that a significant effect was observed for several time units; the effect can be characterized as abrupt and lasted for several weeks after the intervention. Results suggest that police crackdowns can effectively disrupt for cryptomarkets. Seizing parcels negatively affected vendors’ reputation and compromised the trust system on which cryptomarkets are built.
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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 | 0.016 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".