Intelligence Gathering Imperative: A Tool for Successful Security Outfits’ Operation
Bibliographic record
Abstract
This study examines the recent security challenges Nigerians and non-Nigerians encounters in their daily existence to eke a living. These threats are orchestrated by the activities of one of the deadliest terrorist group in the world (Boko Haram), and herdsmen notorious killers, armed banditry, cow rustling by rustlers, incessant kidnapping especially students in schools and unsuspecting Nigerians and non-Nigerians alike; which has made Nigeria federating states unsafe for any meaningful socio-economic development to thrive. To achieve this, two objectives were stated to include, underscore the overarching relevance of intelligent gathering in preventing crime and understand the elaborateness of intelligence gathering that can be utilized to mitigate crimes outcomes. The study found that intelligence gathering is the disconnect that has precipitated this state of affairs in Nigeria. Equally responsible is the people’s loyalty which is first and foremost skewed to ethnoreligious and political considerations instead of the country. It was recommended among other appeals to include: The National Intelligence Agency (NIA) of Nigeria which is the coordinating security agency should be properly funded and personnel trained in modern intelligence gathering techniques, the unnecessary bickering, in-fighting for superiority between and among security agencies in Nigeria should be discouraged because this itself is a big challenge in security equation of Nigeria; and serious effort should be made to stop the politicization of security intelligence gathering matters based on tribe, ethnic group, religious and political affiliations.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".