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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".