Mass Violence, Environmental Harm, and the Limits of Transitional Justice
Bibliographic record
Abstract
The relationship between the environment and mass violence is complex and multi-faceted. The effects of environmental degradation can destabilize societies and cause conflict. Attacks on the environment can harm targeted groups, and both mass violence and subsequent transitions can have harmful environmental legacies. Given this backdrop, it is notable that the field of transitional justice has paid relatively little attention to the intersections between mass violence and environmental degradation. This article interrogates this inattention and explores the limitations and possibilities of transitional justice as a means of addressing the environmental harms associated with mass violence. The article makes four key claims. First, that the "dominance of legalism" in transitional justice has produced anthropocentric understandings of harm which exclude environmental harms and victims. Second, that transitional justice’s tendency towards neo-colonialism has led to the disregarding of worldviews that might encourage more environmentally inclusive responses to violence. Third, that transitional justice’s inability to redress structural inequalities has often left environmental injustices intact. And fourth, that the field’s complicity in normalizing neoliberal capitalism both overlooks environmental harm and facilitates future environmental degradation. In light of these claims, the article considers whether, and where opportunities might exist, for "greener" responses to mass violence.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.096 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| 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".