Bibliographic record
Abstract
Nigeria is a diplomatic force within West Africa, a major participant in continental African politics and an important international actor.As the world's seventh-most-populous country, its 14thlargest oil producer and home to Africa's fifth-largest military, Nigeria possesses tremendous resources.Yet Nigeria's internal security challenges and political dysfunction constrain its role on the regional, continental and world stages.Cyclical violence undermines the rule of law and entrenches intercommunal enmities.Pervasive corruption drains funding from services and infrastructure and saps public confidence in government.Policy implementation often proceeds haphazardly and generates backlash.Finally, "do-or-die" electoral politics, as former President Olusegun Obasanjo characterized the country's voting culture, heightens political violence and elevates political tensions.The insecurity situation in the country has made Nigerians more interested in issues relating to security.For example, it would not be strange to have citizens discussing budget allocation to security and law enforcement agencies, rules of engagement of security operatives in the northern part of the country, operational strategy or procedure of JTF and other security agencies, equipments purchased, watch with keen interest parliamentary debates or discussions in respect of Baga (or any similar situation).This has become so topical that it has become focus of media, academic and NGO reports.This paper seeks to add to the debate.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.980 | 0.980 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".