Disarmament, Demobilisation, and Reintegration: Analysing the Outcomes of Nigeria’s Post-Amnesty Programme
Bibliographic record
Abstract
Disarmament, demobilisation, and reintegration (DDR) programmes are an essential part of most contemporary post-conflict peacebuilding processes, but they are seldom the subject of academic analysis. In this study, we seek to reduce this gap by examining the Post-Amnesty Programme (PAP) introduced in Nigeria in 2009. Our analysis shows that the programme contributed to the reduction of small arms and light weapons (SALW), fewer attacks on oil infrastructure and kidnapping of expatriates, and improved human capacity development. However, the programme has been ineffective in reintegrating ex-militants into civilian life because of serious shortcomings in its design as well as the extremely difficult implementation environment. In addition, the programme has proved to be hugely expensive. Despite these serious shortcomings, the Federal Government of Nigeria cannot simply terminate the programme because this will increase the risk that ex-militants enrolled in the programme will reignite the violent insurgency against the Nigerian state and international oil companies. The study concludes by reflecting on how this challenging situation can be resolved.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".