Civilian Joint Task Force’ (CJTF) – A Community Security Option: A Comprehensive and Proactive Approach of Reducing Terrorism
Bibliographic record
Abstract
Since the Boko Haram uprising in 2009, the Nigerian government has employed various strategies as counter-terrorism measures to stem the atrocities of the group. These strategies include amnesty negotiations, implementation of emergency law in the northeast, increase in security spending to the deployment of military force. In the midst of these security measures, the civilian Joint Task Force (JTF) emerged, first as a community effort, and later as a joint effort with the security forces to help fight Boko Haram. The civilian JTF has helped recover towns and villages from Boko Haram, rescued women in the northeast and helped identify Boko Haram members shielded by some local people. Although doubts have been expressed in some quatres that the civilian JTF could transform into ethnic militias, the Boko Haram security threat neutralized by the group indicates an untapped security potential in Nigerian communities. However, one approach that has yet to be pursued is community security option. Community security option is a model built around proactive citizen-driven communal response. This article explains the role of civilian JTF and how civilian JTF can be used to investigate terrorism in Nigeria. This article will contribute to the discourse on the imperative of African-inspired mechanisms to solving African security problems.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".