All that is Solid Burns into Smoke: US Military Burn Pits, Petrochemical Toxicity, and the Racial Geopolitics of Displacement
Bibliographic record
Abstract
Focusing on US military burn pits in Iraq, this paper traces entanglements between the materials of US war-making, the logistics of global capitalism, and the racialized displacement of toxicity and chemical kinship. In interviews about their experiences of burn pits at Joint Base Balad, a city-sized US military base located in Yathrib, Iraq, US veterans living along the US Gulf Coast linked their exposures to the toxicity of burn pits in Iraq with petrochemical exposures in their everyday lives at home. These links forged a chemical kinship with domestic others, while largely overlooking such kinship with Iraqis who share veterans' body burden. Yet I suggest that in these veterans' attention to logistics and infrastructure lies the possibility of a more expansive account of chemical kinship, one that cuts across the racialized distinctions of foreign and domestic, and gendered imaginaries of the domestic as a comfortable space for the reproduction of homophilic kin. I describe this dual imperative of the domestic as an ideology of domestic security. The toxicity of burn pits helps us to undermine this ideology of domestic security, opening new spaces to reckon with the relation between US and Iraqi experiences of US military toxicity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".