“Like aspirin for arthritis”: A qualitative study of conditional cyber‐deterrence associated with police crackdowns on the dark web
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Research summary Crackdowns are law enforcement strategies based on the principles of deterrence theory, which stipulates that offenders are rational actors who will refrain from crime if perceived risks are higher than perceived benefits. Studies have shown that the effects of police street drug crackdowns are mostly short termed and followed by considerable displacement. In the early 2010s, an important part of illicit drug trades moved online to cryptomarkets, and law enforcement agencies have responded by engaging in online drug crackdowns. In this study, we focus on the perceptions of dark web users in order to determine, from a qualitative “data‐driven” perspective, whether police online crackdowns may have a cyber‐deterrent effect by analyzing 1796 forum posts. Our results show that these events trigger psychological and practical consequences that participants claim to have a conditional, although minor, deterrent effect. In the majority of cases, dark web users claimed to engage in several forms of spatial and tactical displacement. Policy implications Our study suggests that police crackdowns on the dark web have limited, short‐term effectiveness in curbing illicit activities. It proposes that innovative policing approaches such as problem‐oriented policing and “pulling levers/focused deterrence” strategies, which involve identifying key actors and engaging with them, be potentially extended to the dark web. While this approach is promising, it emphasizes the need for further research to assess its efficacy in the online realm, as it is a largely uncharted territory for law enforcement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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