A Profile of the Demographics, Psychological Predispositions, and Social/Behavioral Patterns of Computer Hacker Insiders and Outsiders
Bibliographic record
Abstract
This chapter looks at the literature—myths and realities—surrounding the demographics, psychological predispositions, and social/behavioral patterns of computer hackers, to better understand the harms that can be caused to targeted persons and property by online breaches. The authors suggest that a number of prevailing theories regarding those in the computer underground (CU)—such as those espoused by the psychosexual theorists—may be less accurate than theories based on gender role socialization, given recent empirical studies designed to better understand those in the CU and why they engage in hacking and cracking activities. The authors conclude the chapter by maintaining that online breaches and online concerns regarding privacy, security, and trust will require much more complex solutions than currently exist, and that teams of experts in psychology, criminology, law, and information technology security need to collaborate to bring about more effective real-world solutions for the virtual world.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".