On the Dynamics behind Profit-Driven Cybercrime: From Contextual Factors to Perceived Group Structures, and the Workforce at the Periphery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Through an inductive thematic analysis of semi-structured interviews with experts, this study corroborates key findings on contextual and organisational dynamics behind profit-driven cybercrime. The findings pinpoint three contextual factors influencing individuals to participate in profit-driven cybercrime: lack of legal economic opportunities, lack of deterrents, and drifting means. The findings also highlight how experts perceive group structures of those behind profit-driven cybercrime: as organised, enterprise-like, loose networks, or communities. Experts’ narratives, moreover, emphasise the presence of a workforce at the periphery of cybercrime groups. Such a workforce is not actively involved in developing criminal schemes, yet it helps their orchestration by achieving necessary tasks such as writing texts or developing software. The study results confirm key insights on crime participation related to both cyber and non-cybercrime literature while also raising new research avenues, including questions concerning to what extent those forming the peripheral workforce are willing to participate in cybercrime.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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