The Climate Conflict Trap: Examining the Impact of Climate Change on Violent Conflict in Sub-Saharan Africa
Bibliographic record
Abstract
As recently as 2019, international security officials reported that international state sponsors of terrorism, such as ISIL, were moving into Sub-Saharan Africa. The causal links between climate change and conflict, especially in an understudied and misunderstood region such as Sub-Saharan Africa, are often complicated and ill-defined. In reality, climate change does not unilaterally or unconditionally strengthen terrorist organizations and, by extension, civil conflict. The circumstances of climate change impact the trajectory of violent non-state armed groups in Sub-Saharan Africa through three primary mechanisms that intersect and interact with one another: natural resource instability, colonialism, and the intensity of intra-state tensions throughout a particular region. Through these three primary lenses, it is evident that, in Sub-Saharan Africa, the effects of climate change exacerbate conditions that, in turn, provide a unique, fertile environment for violent non-state armed groups to develop and thrive.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; both teacher heads agree on what is shown here.
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".