A social learning theory and moral disengagement analysis of criminal computer behavior : an exploratory study
Bibliographic record
Abstract
This study was exploratory and examined the characteristics of lndividuars engaged in computer criminar activity.It was predicted that individuaÌs who had engaged in ill-icit computer activity woul-d have higher rates of dífferential association, differential reinforcement, and moral disengagement than non-crj-mina1s.f t was al_so hypothesized that the combinatj-on of differential assoclation, differentialreinforcement, and moral_ disengagement better predict criminal computer behavior than either variabl-e alone.In Phase 1 of the study, a comparative analysis was conducted on demographic data from 132 computer and general criminal-s.In phases 2 and 3, Il-2 Internet participants, and 36 general criminals participants compJ-eted the Computer Crime Index & Sociaf Learning Questionnai-re (Rogers, 2000) , and the paulhus Deception Scal-e (PauJ-hus, 1998) .The hypotheses regarding dj-f f erential_ ¡ < cnr-.ì.ai-inn .iiff erential reinf orCement, and moral disengagement \^Jere supported.However, contrary to the predictions, the reduced modeÌ consisting of moral disengagement and differentialassociation better predict criminal computer behavior.Additional exproratory analyses and the implications for future research are al_so discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".