The FARC-EP as environmental governance actors: shifting the ecological perspective on war
Bibliographic record
Abstract
Abstract Environmental protection is widely considered a core function of the state. Yet more than 210 million people currently live under the control of armed non-state actors (ANSAs), many of whom exercise state-like authority over vast, environmentally important territories. Despite growing legal and political science scholarship on ANSAs, their role in environmental protection remains largely unexplored. International law, shaped by conflict-centric frameworks, often fails to account for ANSAs’ non-military dimensions – especially those related to environmental service provision. Similarly, theories of rebel governance have yet to meaningfully incorporate environmental service provision as a governance facet. The article addresses this gap by examining the Revolutionary Armed Forces of Colombia – People’s Army (FARC-EP) in Colombia, drawing on documentary analysis and interviews with former combatants. It shifts the limited ecological perspective on war, arguing that the FARC-EP’s environmental practices amounted to a form of rebel environmental governance – structured, intentional and legally plural. Through this case study, the article challenges dominant narratives that view ANSAs solely as environmental spoilers or incidental protectors and instead advocates for a more comprehensive understanding of their impact as environmental service providers and lawmakers. In doing so, the paper reframes ANSAs as socio-legal actors whose environmental practices merit scholarly attention – particularly in ongoing debates around accountability and transitional justice in conflict-affected regions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".