AN EXAMINATION OF THE IMPACT OF NATIONAL DEFENCE STRATEGY ON THE WAR AGAINST INSURGENCY IN NIGERIA. A STUDY OF IGABI LOCAL GOVERNMENT AREA IN KADUNA STATE. 2016– 2023
Bibliographic record
Abstract
The growing challenges in Nigeria’s National Security hasbecome a major threat to the overall well-being of everyone living in Nigeria, neighbouring countries and her sovereignty in general. This challenge is closely tied to the lapses inherent in national defence policies and inadequate implementation. The expanded and worrisome attacks by armed insurgent groups within Nigeria cannot be unconnected to uncoordinated national intelligence sharing by authorised agencies, lack of sophistication in security infrastructure, shortage in manpower, advanced information and technological backwardness. This research examines the impact of Nigeria’s national defence policy on anti-insurgency war in Kaduna State and Igabi Local Government Area (LGA) in particular between 2016 and 2023. The research adopted the quantitative analysis and secondary data capture. The research identified that some of the main reasons for the escalated activities of the armed insurgency group is tied to prolonged economic backwardness of most Nigerians, political instability, ethno religious conflict, impunity, and unemployment. The Clausewitz theory of applied strategy has been adopted for this research. In its findings, the Nigeria national defence policy is a much needed strategy that will further pull the victory in the war on insurgency much closer if adequately implemented. The study recommends the need for professionalization and mobilization of all national security apparatuses as key to her sovereignty.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".