Data-driven conflict classification exposes weak predictive indicators
Bibliographic record
Abstract
Abstract Models and theories of armed conflict are effective when tailored to distinct conflict types, but existing classifications are often heuristic. We introduce a data-driven classification that is empirically grounded, reproducible and consistent across multiple scales. We leverage fine-grained conflict data, which we map to climate, geography, infrastructure, economics, raw demographics and demographic composition in Africa. Using an unsupervised learning model, we identify three overarching conflict types: ‘major-unrest’ at densely populated, riparian regions with well-developed infrastructure; ‘local-conflict’ in moderately populated, socio-economically diverse regions and often confined within country borders; and ‘sporadic-spillover events’ in low-population, underdeveloped areas. The three types stratify into a hierarchy of factors that highlights population, infrastructure, economics and geography, respectively, as the most discriminative indicators. Specifying conflict-type negatively affects the predictability of conflict intensity such as fatalities, conflict duration and other measures of conflict size. The competitive effect is a general consequence of weak statistical dependence. Hence, the empirical and bottom-up approach reveals how armed conflicts stratify into three archetypes, yet cautions us about the inclusion of commonly used indicators into predictive modelling.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".