Book Review: <em>Scorched Earth: Environmental Warfare as a Crime Against Humanity and Nature</em>
Bibliographic record
Abstract
The subtitle of Emmanuel Kreike’s Scorched Earth foreshadows the goal of this impressive and comprehensive contribution to the field. His goal is to chip away at the Nature-Culture dichotomy that he argues drives, and limits, much of the analysis that is produced of historical, and modern, warfare. Kreike uses the concept of environcide, which he defines as “intentionally or unintentionally damaging, destroying, or rendering inaccessible environmental infrastructure”, and argues that the traditional assumptions about nature and culture in the study of warfare obscure the importance of the natural world in determining who lives and who dies. For the field of genocide studies, Kreike’s work promotes the analysis of mass violence and potentially genocidal conflicts by looking not simply at actions taken by perpetrators directly against victims, but also at a litany of actions that perpetrators might take that could reasonably result in mass death, joining those in the field who promote a shift in the definition of genocide that includes actions that do not simply meet the definition of dolus specialis to also those that demonstrate dolus eventualis. While confiscating food and burning fields may not fit our current understanding of genocidal acts, they can certainly have the same eventual outcome as the use of machine guns and poison gas. And, recent scholars of risk factors do note the importance of “crises, resource scarcity, population pressure, natural disasters” as increasing the likelihood of genocide.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.092 | 0.062 |
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".