Community leaders as determinants of conflict and peace: understanding the causes and spatial variation of ethnic conflict in Jos, Nigeria
Bibliographic record
Abstract
Jos, a Middle Belt Nigerian city, is commonly referred to as the hotbed of ethnoreligious conflicts in Nigeria. In the post-independence era, the city has been bedevilled by four major conflicts between the mostly Christian indigenous Berom ethnic group and the predominantly Muslim settler Hausa and Fulani ethnicities. The 2000s saw recurrent fighting between these groups in the city, and Jos has remained turbulent since then. Yet, not all the Jos communities that are inhabited by these ethnic groups have been involved in the conflict. Both Angwan Doki and Dadin Kowa are, for example, inhabited by Berom, Hausa and Fulani, populated by Christians and Muslims and relatively low-income communities. Yet, only the former was enmeshed in intergroup conflict between 2001 and 2010. Informed by the phenomenological approach’s requirement of “minimum structure for maximum depth,” I explored the experiences of intergroup relations of 12 participants in each community in order to understand how Dadin Kowa avoided the conflict even though neighbouring Angwan Doki was involved in it. With semi-structured interviews as my main research instrument, I explored people’s relational experiences pre, during and post-conflict in order to produce a comprehensive view of its social environment. To make sense of the unearthed stories, I constructed a model of understanding using the General Inductive Approach. My model of understanding, which consists of a causal network and a temporal sequence, indicates that ethnicized electoral politics is the epicentre of the causal conditions in both communities yet the interventions of the Dadin Kowa community leaders halted their progression to violent intergroup conflict there.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".