The Boko Haram Insurgency in Nigeria: What could have been the precursors?
Bibliographic record
Abstract
The problem of insurgency has for several decades occupied a good part of the attention of IR scholars. This paper explores the various perspectives on the formation and radicalization of Boko Haram in Nigeria. The focus is on the extent to which illiteracy, unemployment, poverty, weak state capability, the almajiri crisis and the mobilization of ethno-religious identity explain simmering insurgency in Nigeria. The group has experienced ferocious onslaught on their activities by the Nigerian Military. The article relies on secondary data. This has enabled the author to draw heavily from literature espousing the diverse perspectives put forth as explanations for the uprising. Fragile state theory serves as a framework for analysis. On this basis, the article demonstrates the low-cost availability of foot soldiers from the almajiri pool, resulting from the state’s inability or unwillingness to provide better education, and employment opportunities, and widespread poverty has exposed youths to indoctrination, criminalization and terrorism. In order to ensure the effectiveness of counter terrorism efforts, the military option should not be solely relied on. Rather, efforts should be geared towards addressing the various underlying social, political and economic triggers of violent insurgency, especially in northern Nigeria where such triggers are pervasive.
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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 | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".