Susan L. Smith. Toxic Exposures: Mustard Gas and the Health Consequences of World War II in the United States.
Bibliographic record
Abstract
Both historians and the public tend to associate the history of chemical warfare with World War I. As Susan L. Smith reminds us in Toxic Exposures, however, mustard gas also played a significant role in World War II, “far greater than most of us realize” (2). While none of the combatant nations employed the weapon directly against enemy forces, Smith writes, “the United States and other Allied nations conducted mustard gas experiments as part of the militarization of medicine and the medicalization of war” (21). As part of an effort to “upgrade older military technologies and create new ones,” the United States and the United Kingdom “exposed thousands of their own servicemen to poison gas as part of their preparation for chemical warfare” (2). This brief, thoroughly researched account (indeed, notes constitute nearly a quarter of the manuscript) provides the most detailed study to date on the topic. In addition to using sources from multiple archives in the United States and Canada, Smith makes extensive use of testimony provided by 250 U.S. veterans who shared their stories as part of public hearings on the matter in 1992. These soldiers and sailors were not well informed about the testing and were not asked for their consent. Instead, Smith notes, “the scientific method required evidence, and the military provided the necessary human bodies” (22). Although it is difficult to ascertain exactly how many service personnel were exposed as a result of the testing, Smith estimates that the number is likely well above the 60,000 Americans first reported by journalist Karen Freeman in the early 1990s, and includes thousands more Canadians, Britons, and Australians (2, 25).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".