Bibliographic record
Abstract
The Internet and computer-mediated communications (CMCs) have drastically changed the way that individuals communicate and share information across the globe. Over the last two decades, financial institutions, private industry, and governments have come to rely on technology in order to access sensitive data and manage critical infrastructure, such as electrical power grids. As a consequence, the threat posed by cybercriminals has increased dramatically and afforded significant opportunities for terrorist groups and extremist organizations to further their objectives. The complex and intersecting nature of both crime and terror make it difficult to clearly separate these issues, particularly in virtual environments, due to the anonymous nature of CMCs and challenges to actor attribution. Thus, this study examines the various definitions for physical and cyberterror and the ways that these activities intersect with cybercrime. In addition, the ways that terrorists and extremist groups use the Internet and CMCs to recruit individuals, spread misinformation, and gather intelligence on various targets are discussed. Finally, the uses of computer hacking tools and malware are explored as a way to better understand the relationship between cybercrime and terror.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| 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".