Armed rebel groups engage in climate governance
Bibliographic record
Abstract
Abstract An estimated two billion people live in areas presently affected by fragility, armed conflict, and violence. In many of these locations armed non-state actors (e.g. rebel groups) rather than the state are the primary governors (Word Bank 2024). An estimated 66 million live under the direct rule of armed non-state actors (Breslawski in J Glob Secur Stud 7(1):ogab017 2022). With the growing severity of climate impacts, armed non-state actors are increasingly engaging in governing over many aspects of climate change, including adaptation, displacement assistance, and the management of natural resources. By revealing the extent and range of these activities, we argue for the need to improve our understanding of the behaviours and motivations of non-state actors, especially the complex ways that climate governance is being integrated into their other —often violent—strategies. Better positioning armed non-state actors within the set of actors who provide climate governance is critical to supporting climate-resilient development to the populations who live in these areas while also managing the ethical and security dilemmas of engaging with these violent actors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".