Nomination Violence in Uganda’s National Resistance Movement
Bibliographic record
Abstract
Abstract Institutional explanations of intra-party violence rarely address political economy dynamics shaping the institutions in question, and therefore they fail to understand their emergence and their stability. Specifically, focusing on institutional factors alone does not enable a nuanced understanding of candidate nomination violence and why some constituencies are peaceful while others are violent. This article theorizes nomination violence in dominant-party systems in sub-Saharan Africa. Drawing on political settlement theory, it examines the nature of nomination violence in Uganda’s October 2015 National Resistance Movement (NRM) primaries. We argue that the violence is a constitutive part of Uganda’s political settlement under the NRM. Nomination procedures remain weak in order for the NRM ruling elite to include multiple factions that compete for access while being able to intervene in the election process when needed. This means, in turn, that violence tends to become particularly prominent in constituencies characterized by proxy wars, where competition between local candidates is reinforced by a conflict among central-level elites in the president’s inner circle. We call for the proxy war thesis to be tested in case studies of other dominant parties’ nomination processes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".