Dangerous environments: environmental peacebuilding’s technomoral imaginary and its power-knowledge effects
Bibliographic record
Abstract
In this article we critically analyze the emerging academic field and practice of environmental peacebuilding. We claim that both are saturated by a particular “technomoral imaginary” or a set of beliefs, normative assumptions, and views on desirable futures that betray unwavering faith in the power of science and technology to bring peace and development to the Global South by transforming environmental governance. This imaginary informs particular rules of knowledge production that work to establish environmental peacebuilding as a conceptually narrow and self-referential field. Zooming in on an environmental peacebuilding project in eastern Democratic Republic of Congo, we demonstrate how the self-referentiality of knowledge production within the field leads to inadequate analyses of key drivers of conflict and violence. Moreover, it blinds scholars and practitioners to the broader power-knowledge effects of environmental peacebuilding, including its complicity in conjuring up “dangerous environments.” By the latter, we refer to the portrayal of environments in the Global South as potential security threats due to various lacks and deficiencies ascribed to these regions, which contributes to the reproduction of a “global environmental color line.” The conjuring up of dangerous environments embeds environmental peacebuilding within a Global-North dominated, colonially influenced apparatus of security and development whose interventions integrate places more firmly into circuits of global capitalism and global governance. To reckon with these power-knowledge effects, environmental peacebuilding must display more self-reflexivity regarding the politics of knowledge production on the Global South.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.101 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| 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".