The Paradox of Cybercrime Risk and Internet Use in Canada: A Socio-Criminological Perspective
Bibliographic record
Abstract
Increased internet use has created opportunities for criminality. Internet penetration is soaring against the backdrop of the increased risk of cybercrime victimization. A mixed method approach is employed in this study to examine why Canadians continue to use the internet, notwithstanding the fear and persistent or inherent risk of victimization in cyberspace, through the lens of the structure and agency discourse. In other words, has the perception of cybercrime victimization changed internet use? Questions explored include: internet users’ knowledge and perceptions of risk, their attitude to internet security, fear of cybercrime and victimization experiences and its impact on behavior, attitude towards reporting, frequency of internet use and effect on behavior, and motivation for internet use. The study utilizes an integrated theoretical framework comprising risk, structuration, and rational choice. Canada-wide data was collected using an online survey. Logistic regression is used to construct models for each outcome variable, while internet use motivation is analyzed using thematic qualitative analysis. The findings indicate that socio-demographic characteristics such as gender, level of education, and marriage are associated with the risk of cybercrime. While victimization experience is significantly negatively associated with cybercrime fear, it is not significantly associated with cybercrime risk perception. The effect of victimization on internet behavior constraints in terms of avoidance and defensive internet use is inconclusive; similarly, there is an inconclusive relationship between fear of cybercrime and cybercrime incident reporting. It is unclear whether internet use frequency constrains the behavior of internet users, even though the univariable results suggest that frequent internet users have increased odds of adopting avoidance and defensive internet use actions. Thematic analysis revealed motivations for internet use to include education and knowledge acquisition, entertainment and fun, communication and social media access, commercial purposes, work and personal related reasons, news and information access, and others. The thematic results demonstrate that an intricate interaction between structure and agency underpins Canadians’ motivations for using the internet. Based on the findings, the study argues for greater theoretical flexibility to understand the apparent paradox between internet use and cybercrime risk. The results have implications for theory, policy and practice, including for cyberspace offending, victimization, and crime control.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".