Extending drift theory to cybercrime forum participation: the case of digital workers
Bibliographic record
Abstract
This study extends Matza’s concept of drift to cybercrime forum participation, suggesting that participants exist in a liminal state where they are neither fully compliant with legal norms nor explicitly engaged in criminal activity. In this state, criminal involvement can only be confirmed when individuals openly disclose crimes on these forums. This nuance is valuable when studying those who are not fully committed to cybercrime, but remain active in these settings, such as digital workers. Through an analysis of 105 digital workers in cybercrime forums, this study reveals their limited and sporadic engagement, with many contributing benign or ambiguous content. This reflects the neutrality of IT, where criminal intent is often ambiguous. Only a small fraction displayed consistent criminal involvement. Moreover, the findings empirically support Goldsmith and Brewer’s (2015) notion of digital drift, underscoring the fleeting and episodic nature of, in this case, cybercrime forum participation.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".