Bibliographic record
Abstract
The Nigeria Police Force first participated in Peacekeeping Operations in 1960 with the deployment of personnel to Congo and ever since the list of countries with Nigerian peacekeepers is now endless.Her peacekeepers have participated in operations in Congo, Namibia, Angola, Western Sahara, Cambodia, Mozambique, Somalia, Rwanda, Yugoslavia, Bosnia-Herzegovina, Croatia, Macedonia, East Timor, Kosovo, Sierra Leone, Liberia, Afghanistan, Cote D'Ivoire, Burundi, Haiti, Sudan, South Sudan, Guinea Bissau and most recently in Mali.To better organize her peacekeeping operations, the Peacekeeping office of the Nigeria Police Force was established in 2005 with a clear vision and mission which include, equipping personnel with requisite skills and competencies required to meet complex peace support operations environment through the delivery of quality internationally recognized and professional training.Since then, the Force has made giant strides in their areas of operations.It has equally faced many problems and challenges.This paper addresses these identified issues and concludes by positing that Peace-building in consonance with its infrastructure is a more sustainable approach to ensuring regional peace and stability and, therefore, ensuring development for the peoples of West Africa.Nigeria should always stride to succeed in learning from her peacekeeping experiences and making her experiences the source of even greater power.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.976 | 0.976 |
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".