The nature of peace: trajectories of environmental peacebuilding between dominant narratives and power relations
Bibliographic record
Abstract
Peacebuilding initiatives play an important role in the reconstruction of political, economic, and social conditions after internal armed conflicts. If these initiatives also account for environmental aspects, they have the potential to address both ecological damages and resource-related conflict causes. The sprawling literature on environmental peacebuilding therefore stresses the need for a holistic perspective to study conditions for sustainable peace. Recent scholarly work has directed attention to peace trajectories that draw connections between political, socioeconomic, and environmental dimensions of peacebuilding processes. We contribute to this growing research field by synthesizing key findings from the inter-disciplinary research program the “Nature of Peace” and related studies. Our goal is to identify, explain, and understand the trajectories that environmental peacebuilding processes may take in societies after an internal armed conflict. We first introduce the theory-based analytical framework that guided our studies. Based on this, we study trajectories that lead from the integration of environmental concerns in peacebuilding efforts to environmental and social consequences of these efforts and to chances for sustainable peace. We apply this framework to a range of geographical contexts, drawing, inter alia, on Colombia since the signing of the peace agreement in 2016, and environmental peacebuilding efforts in Uganda since 2002. We find that both constellations of power and dominant narratives considerably shape the routes that environmental peacebuilding may take. In particular, dominant nature-neglecting, instrumentalizing, and extractivist narratives have far-reaching impacts on the trajectory of peace processes. This is not only with regard to their environmental implications, e.g., deforestation and biodiversity loss, but also social consequences, e.g., threats to livelihoods of vulnerable communities, and, ultimately, old and new forms of physical, structural, and cultural violence. Thus, although addressing power asymmetries remains an important step for environmental peacebuilding, it is essential to counter narratives that instrumentalize nature to open new alleys for sustainable peace.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".