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 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".