Examining the Effect of Victimization Experience on Fear of Cybercrime: University Students' Experience of Credit/Debit Card Fraud
Bibliographic record
Abstract
<em>Fear of crime research tends to focus disproportionately on physical or place-based crimes while cybercrimes, which have been increasing over the past two decades, are relatively excluded. Drawing on Beck’s theory of a risk society, this paper examines the impact of previous victimization experiences on fear of future encounters with cybercrime. A total of 462 students at the University of Saskatchewan participated in an online survey that collected demographic information and asked if they had ever felt fearful about being the victim of credit/debit card fraud. Binary logistic regression was used to predict fear of cybercrime victimization. Prior experience of victimization was positively associated with students’ fear of becoming victims of credit/debit card fraud. Socio-demographic factors and knowledge of cybercrime were not significant predictors of students’ fear of becoming victims of credit/debit card fraud. This study highlights the need to reconsider risks and examine reflexivity further as it relates to how people modify their behaviors when faced with the threat of cybercriminal victimization. This study also highlights the need for fear of crime research, and victimology in general, to consider the unique differences between the different crime forms – conventional and cyber-based crimes. </em>
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".