Bibliographic record
Abstract
Existing cybercrime research in the information systems (IS) field has focused on a subset of corporate incidents (e.g., fraud, hacking intrusions), and emphasized solutions designed to repel attacks or to minimize their aftermath (e.g., barrier technologies, enhanced security procedures). This focused, defensive, and pragmatic posture is valuable and necessary as an immediate triage response, to "stop the bleeding" and provide protection from imminent harm. However, the extant work has not painted a sufficiently broad picture of the scope of cybercriminal activity, nor paid adequate attention to its root causes. This paper presents a different view. It analyzes 113 U.S. Department of Justice federal cybercrime cases from 2008 and 2009, categorizes these cases using an applied criminal offense framework developed by the FBI, considers philosophical explanations for criminal motives, and then identifies the apparent motive(s) that led to the commission of each crime. This paper seeks to contribute to an improved understanding of what cybercrime is, and why it is occurring at the individual level, in order to develop more proactive and effective solutions.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".