Cyber-Dependent Crime Victimization: The Same Risk for Everyone?
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 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