<i>Scientists at War: The Ethics of Cold War Weapons Research</i> by Sarah Bridger
Why this work is in the frame
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Bibliographic record
Abstract
Scientists at War: The Ethics of Cold War Weapons Research, by Sarah Bridger. Cambridge, Harvard University Press, 2015. x, 350 pp. $45.00 US (cloth). Fresh off success of Manhattan Project and troubled by role in creating atomic bomb, US scientists attempted to guide national security policy away from reliance on nuclear weapons. Science advising a chance for nuclear redemption in form of arms (24), Sarah Bridger explains in Scientists at War, an analysis of how science advisors tried to cope with ethical burdens of (270) during Cold War. Science advisors used technical expertise to advocate for nuclear arms control and diversification of conventional weapons as a counter to Dwight Eisenhower's New Look policy that relied exclusively on nuclear weapons and risked nuclear war. But as Bridger makes devastatingly clear, this influence over policymaking ultimately backfired: policies and technologies that scientists advocated, such as limited warfare, anti-infiltration systems, and chemical and biological weapons were seen as ethical alternatives to nuclear war, but were ultimately used to unleash incredible violence on Vietnam that was nearly as troubling as Hiroshima had been. During 1960s, meanwhile, a new generation opposed science's connection to folly and brutality of Vietnam. Along with challenging science's links to defense establishment, younger scientists reassessed discipline from an activist, ethical perspective, and in process questioned Pentagon funding, apolitical stance of professional organizations, and fundamental concepts such as objectivity. According to Bridger, these two generations reconciled conflicting paradigms when they united to oppose Strategic Defense Initiative (SDI) in 1980s, as a near-consensus of scientists young and old opposed system on both ethical and technical levels. The book powerfully illustrates dilemma of political power. On a detailed level, though, nuance is lost. Bridger argues that government scientists used language to support anti-nuclearism, but offers an overly broad definition of moral, describing, for example, a scientists' statement that a nuclear test ban was in the best interests of United States and world peace as a moral message (54). Such a claim, however, was typical Cold War pabulum that differed tremendously from language of outsider scientists such as Linus Pauling, who morally castigated American people as murderers, mass murderers for testing nuclear weapons [No More War!, New York, 1983, 215). In fact, government scientists purposefully distanced themselves from moralists like Pauling. When science advisors produced a study arguing that nuclear weapons would not help achieve US goals in Vietnam, they used language scrubbed of any or ethical taint, although conclusions came from their values and commitment to preventing nuclear war (133-134). …
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it