Political Fragmentation and Alliances among Armed Non-state Actors in North and Western Africa (1997–2014)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Drawing on a collection of open source data, the article uses network analysis to represent alliances and conflicts among 179 organizations involved in violence in North and Western Africa between 1997 and 2014. Owing to the fundamentally relational nature of internecine violence, this article investigates the way the structural positions of conflicting parties affect their ability to resort to political violence. To this end, we combine two spectral embedding techniques that have previously been considered separately: one for directed graphs that takes into account the direction of relationships between belligerents, and one for signed graphs that takes into consideration whether relationships between groups are positive or negative. We hypothesize that groups with similar allies and foes have similar patterns of aggression. In a region where alliances are fluid and actors often change sides, the propensity to use political violence corresponds to a group’s position in the social network.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it