Unabated Cyber Terrorism and Human Security in Nigeria
Bibliographic record
Abstract
The development of Information and Communication Technology (ICT) due to Internet connectivity has called to question the preparedness of nations to curb cyber terrorism and the effects on human security. Boko Haram emerged as one of the deadliest terrorist groups globally. The paper investigated the Nigeria’s efforts in checkmating cyber terrorism, the implication on human security and the inherent challenges associated. The paper employed descriptive research and qualitative method while secondary sources of data were adopted. The study found that cyber terrorism as employed by Boko Haram was deployed in raising fund, propaganda, coordinating operation, international collaboration, recruitment and training of its members. In addition, the Nigerian government has not given sufficient attention to war against cyber terrorism and this has complicated human security provisioning particularly in the North-East Nigeria. This study concluded that cyber terrorism has come to stay as long as development in ICT cannot exclude the terrorists and the prevalence of fear of attack and the destruction of lives and property facilitated by Internet have devastating effects on human security. This paper recommended the criminalization of terror attacks, adequate equipment of the security agencies and political will to tackle societal ills.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".