Bibliographic record
Abstract
Contemporary spatial research on civil conflict in Sub-Saharan Africa has largely focused on border regions as spaces of limited political and economic opportunity. These studies largely adopt approaches that present borderlands as institutionally desolate regions lacking in governance, economic opportunity and political inclusion and giving rise to the feasibility of rebel conflict. While spatial analyses focus on territorially-based capabilities, such as state power projection, they typically overlook borderlands and their territorial distinctiveness with regards to rebel capabilities. This paper specifically explores the structural effects of borders on rebel capabilities and argues that Sub-Saharan Africa’s porous borders enhance the capabilities of rebels to operate in nearby territories. I empirically test this hypothesis with a zero-inflated negative binomial model and spatially disaggregated conflict events data from the Armed Conflict Location & Event Data Project dataset mapped to the PRIO-GRID 0.5-degree x 0.5-degree geographic data structure. In total, the analysis covers 14,120 georeferenced rebel conflict events in 37 countries between 1997-2019. The results provide strong evidence that territories nearer to borders are likely to experience more battle events relative to other territories, suggesting that borderlands may enable distinct conflict-related capabilities for rebels not found elsewhere. Additionally, the model also differentiates the effects that the border may have on conflict, testing the effect of rough terrain, resources, excluded groups, and towns at the border. Of the variables tested, the results suggest that territories with border towns significantly increase the capabilities of rebels to engage in conflict and suggest a more nuanced scholarly consideration of cross-border institutions that facilitate rebel conflict.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".