Cyber-Psychopathy Revisited: Theorizing Contextual Gaps in Offending between Online and Offline Spaces
Bibliographic record
Abstract
Cybercrime scholarship has historically relied on traditional criminological theories to explain online offending and victimization. However, the Internet represents a social context with unique structural conditions including anonymity, asynchronicity, fewer non-verbal cues, and less salient norms that may afford different criminal opportunities than face-to-face environments. From this perspective, I propose new theorizing frameworks that explicitly consider how the structural differences between online and offline spaces may underlie different offending outcomes between these contexts. In particular, I offer an application of “context-congruent” theorizing by revisiting my original cyber-psychopathy theory, which hypothesizes that some people respond to different opportunities and constraints on the Internet by dissociating into a deviant persona that favours online offending. I argue that this process can involve a shift in one’s online personality expression toward higher levels of primary and secondary psychopathic traits, such as a lack of empathy and greater impulsivity. I also show that this theory can improve our understanding of “contextual gaps” in which online misbehaviours are deemed more acceptable and/or are more likely to be performed than similar offline transgressions. Ultimately, this dissertation outlines my efforts to improve and validate the measurement of cyber-psychopathy and then to empirically test whether this theory can explain outcomes related to both online offending and contextual gaps in offending. My data come from two web-based surveys collected via Amazon Mechanical Turk (N=314) and a Qualtrics online panel (N=960). Quantitative analyses address the following: 1) the possibility of between-context differences in one’s psychopathic trait expression, 2) the predictors of online increases in these traits, 3) the effects of cyber-psychopathy on the acceptability and likelihood of online offending, 4) the role of cyber-psychopathy as mediating the relationship between structural-level predictors and online offending outcomes, and 5) whether online increases in psychopathic traits predict wider contextual gaps. While many results provide evidence in favour of the cyber-psychopathy theory, others introduce complexities that need to be unpacked in future research. Alongside these findings, I demonstrate the value of developing new theories that consider the criminological affordances of cyberspace and I highlight some practical implications arising from thinking “structurally” about online offending.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.006 |
| 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".