Bibliographic record
Abstract
Terrorism has been a constant threat in traditional and contemporary societies. Recently, it has been converged with new media technology and cyberspace, resulting in the modern tactic, cyber-terrorism, which has become most effective in achieving terrorist goals. Among the countless cyber-terrorist cases and scenarios of only this last decade, the paper discusses four cyber-terrorism cases that represent the most recent severe cyber-terrorist attacks on infrastructure and network systems—Internet Black Tigers, MafiaBoy, Solo, and Irhabi 007. Regardless of the nature of actors and their motivations, cyber-terrorists hit very aggressively causing serious damages. Cyber-terrorists are rational actors who use the most advanced technology; hence, the critical need for the use of counter-threat swords by actors on the other side. Given that terrorist goals are mostly dependent on the media’s reactions, journalistic practices are significant and need to be most effective. A major tool that can help journalists in their anti- and counter-terrorist strategies with cyber-terrorists is rationalism, merged with the expected socially responsible conduct. Rational behaviour, founded in game theory, along with major journalistic ethical principles are fundamental components of effective media decision-making during times of terrorism.
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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".