Cybercrime and Strain Theory: An Examination of Online Crime and Gender
Bibliographic record
Abstract
Purpose: Historically, cybercrime has been seen as a near exclusively male activity. We were interested to learn whether the relationship between strain and crime holds for both males and females. Methods: We utilized an online survey instrument to collect data from a national sample of individuals (n=2,121) representing the US population by age, gender, race and ethnicity. We asked offending related questions regarding various cybercrimes. In the current study, we use data from 390 individuals who reported a cybercrime activity within the past 12 months. Results: We find strong support for prior strains correlating with both specific (e.g., illegal uploading) and general cyber-offending. We further examine whether gender interacts with strain. While general strain theory (GST) correlates with cyber-offending for both males and females, we did find a few important differences. Except for lack of trust in others and receiving unsatisfactory evaluation at school or work, there are different variables responsible for online offending for men and women. Parents’ divorcing, anonymity, and online video gaming increase cybercrime offending in women, whereas falling victim to a crime, breaking up with a significant other, and darkweb activity are correlated with cyber-offending for men. Conclusion: Although GST functions differently by gender when it comes to engaging in cyber-offending, the theory is indeed gender-specific, as different strain variables are responsible for engaging in cyber-offending in women and men. Components of general strain responsible for cyber-offending need to be further studied concerning gender. According to our results, GST is gender-specific, and these variables need to be further studied.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".