Towards Understanding the Boko Haram Phenomenon in Nigeria
Bibliographic record
Abstract
Current international media reports on Nigeria indicate that it is having some serious security challenges arising from the destructive activities of Boko Haram insurgent group. Furthermore, similar reports show that attempts by the Nigerian government to overcome the problem appear to be in effective resulting in loss of several lives and properties almost on a daily basis. Thus, prompting the government to seek for international support from United States of America, China and other European states. Against these aforementioned developments in the country, this paper attempts to examine the evolution, operational strategy, effects of Boko Haram insurgent group’s activities and response of the Nigerian government. The paper employed qualitative research method and specifically used content analysis to review existing secondary data relating to Boko Haram phenomenon. Among the findings of the paper is that, Boko Haram phenomenon which emerged in 2002 within the north-eastern state of Borno as a peaceful religious sect has been transformed into a deadly terror organization. Political, external forces and lack of comprehensive approach by the Nigerian government have been identified as some of the factors which contributed in worsening the situation. As a result several lives and properties have been lost. It is therefore, the opinion of the paper that while attempting to overcome the challenges posed by the insurgent group, Nigerian government should at the same time begin to address some of the issues that led to their emergence and spread.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".