Terrorism Financing in Nigeria: Its Roots, Impacts, and Possible Reforms
Bibliographic record
Abstract
The threat of terrorism financing has left a trail of violence and conflict in Africa which prevents countries like Nigeria from meeting important goals such as the United Nations Sustainable Development Goals and the African Union Agenda 2063. There is an urgent need for action to address the challenges that this threat represents to the fragile stability of Nigeria. This paper uses data from local and international journals, investigative reports, online newspapers, government publications, and conference articles, to describe the problematic and endemic nature of terrorism financing in Nigeria. The paper further evaluates the magnitudes of terrorist funding and its impacts on people's socio-economic conditions. The research indicates that weak borders – particularly in the northern region of Nigeria – along with religious bigotry, poor governance, and high unemployment and poverty rates are some of the key enablers of terrorism financing. To bring the problem under control, the paper suggests that building close collaboration with neighboring countries, overhauling security intelligence, resolving people's socio-economic crises, embracing innovations informed by research and development, and strengthening counter-terrorism policies are central to curtailing the outrage of terrorism financing in Nigeria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".