Terrorism and Counter Terrorism in Nigeria: Theoretical Paradigms and Lessons for Public Policy
Bibliographic record
Abstract
The hemorrhagic acts of the Boko Haram and Niger Delta militants in Nigeria warrants an exhaustive discourse on terrorism and counter terrorism in Nigeria. This paper argues that countering terrorism in Nigeria involves understanding the nexus of extremism and criminality in the political, social and religious spheres. It argues in line with Cockayne (2011) that there is a growing recognition internationally that criminal networks (both local and transnational) threaten not only to fuel violent conflict, but also to undermine democratic gains-by criminalising politics and instrumentalising continuing disorder. This author addresses this issue using literature evidence and ethno-methodology (ethnography and historiography) to advance this discourse. The author reviews historical evidence on terrorism, proposes some relative theoretical explanations and suggests economic, security and socio-psychological measures to counter terrorism in Nigeria.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".