From Design to Implementation: Addressing the Causes of Violent Conflict in Nigeria
Bibliographic record
Abstract
This article considers the ways in which knowledge and research influenced the design of a programme to reduce violent conflict in Nigeria. The diversity of sources and forms of conflict in Nigeria, and the way that local grievances interact with national struggles over politics and resources, combined with a need to show measurable results within five years, made the task of programme design extremely challenging. The article discusses how the project design team responded to this challenge. It describes the four main lessons that emerged from dialogue-based research studies that helped the design team formulate a theory of change for the programme, and subsequently its methodological approach and activities. The studies shaped the central theme of the project, which was the need to transform conflict management institutions into genuinely inclusive forums for dialogue, thereby regaining the trust of those currently excluded from dialogue but yet most affected by violence – particularly unemployed youth and women and girls. The article does not portray research and knowledge simplistically, as the sole solution to project design issues. Rather, it shows that if research findings can take designers directly to the core of the problems as perceived by those most affected by them, then they can play a critical role in designing appropriate interventions and, as implementation proceeds, to demonstrating progress towards project goals.
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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 | 0.062 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".