Cyber-Dependent Crime Victimization: The Same Risk for Everyone?
Bibliographic record
Abstract
The Internet has simplified daily life activities. However, besides its comfortability, the Internet also presents the risk of victimization by several kinds of crimes. The present article addresses the question of which factors influence cyber-dependent crime and how they vary between three kinds of cyber-dependent offences: malware infection, ransomware infection, and misuse of personal data. According to the Routine Activity Approach, it is assumed that crime is determined by a motivated offender, the behavior of the Internet user, and the existence of prevention factors. Our analyses were based on a random sample of 26,665 Internet users in two federal states in Germany, aged 16 years and older; 16.6 percent of the respondents had experienced at least one form of cyber-dependent victimization during the year 2014. The results indicate that individual and household factors, as well as online and prevention behavior, influence the risk of cyber-dependent victimization. Furthermore, the effects differ between the three types of offences. In conclusion, the risk of being victimized by cyber-dependent crime is not the same for anyone, but depends on multivariate factors according to the idea of Routine Activity Approach. However, in view of the fact that crime-related factors also matter, studying different cybercrime offences separately seems to be an appropriate research approach.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".