Subnational ethnic conflict: the case of the African Great Lakes Region
Bibliographic record
Abstract
Abstract Debates over the links between ethnicity and conflict often focus on the national level and take an ahistorical approach. This approach hides cases of ethnic conflict that arise at the subnational level and leaves unanswered questions over how ethnicity became a driver of conflict. This article explores these blind spots, using three cases in the African Great Lakes region. The cases reviewed here are the bipolar situations of Hema v. Lendu in Ituri (DRC), Banyarwanda/Banyamulenge v. ‘Autochthons’ in South and North Kivu (DRC), and Hima v. Iru in Ankole (Uganda). These cases suggest that polarisation is a more useful approach than fragmentation, but simple correlations between ethnic dyads and conflict obfuscate the nature and depths of the cleavages, as well as the mechanisms fuelling them. We elaborate on the pathways of escalation, highlighting how and when elite manipulations can activate deeply held identitarian norms. We conclude by emphasising the many lulls and moments of de-escalation, countering the portrayal of ethnic conflict as somehow inevitable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".