Firearms analogies and settler colonialism in US nuclear deterrence strategy
Bibliographic record
Abstract
Abstract Nuclear strategy has long been formulated through analogies. We focus on one in particular: guns. Early nuclear strategists in the United States used multiple analogical comparisons to make sense of the new, apparently unprecedented technology that confronted them. They compared nuclear deterrence to gun dueling and the nuclear revolution itself to the rise of gunpowder on European battlefields. Both analogies invoked empire, in the form of American settler frontier gunfights and the impact of firearms on European expansion. This article offers a critical reading of them. We show both analogies were historically flawed, relying on outdated accounts of how firearms shaped military-political change. Our argument proceeds in three stages. First, we document the role of gun analogies in early US nuclear strategic writing. Second, we critically evaluate the analogy, showing its historical and analytical limits. Drawing on firearms literatures in history, sociology, criminology, and economics, we show that much of what we now know about firearms diverges from nuclear theory and history. Third, we develop an alternative interpretation, contrasting these analytical fictions with the actual history of nuclear colonialism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".