Bibliographic record
Abstract
The U.S. takes great pride in conducting war ‘humanely’, but is humane warfare an achievement to celebrate or a cynical contortion of incommensurable principals? This chapter reviews Samuel Moyn’s Humane: How the United States Abandoned Peace and Reinvented War and advances its own arguments in relation to the concept of humanity. Moyn presents a compelling account of the costs associated with humanising warfare, not least by connecting it with the demise of peace and anti-war politics. But we suggest that the scope of humanity in war is broader and more complicated than Moyn suggests. Our argument rests on two developments in humane war that Moyn dismisses or overlooks. First is the development of rules on the regulation of hostilities in the nineteenth and twentieth centuries, which we argue constitute and calibrate military force in broadly legitimate and ethical terms that prefigure the post-Vietnam War era of humanisation that animates Moyn’s analysis. Second is the turn toward humanitarian wars that emerged in the late twentieth century, and which we argue is an important transformation where ‘humanity’ becomes not only the means but also the justificatory ends of war. The chapter begins with an overview and contextualisation of Moyn’s argument before discussing these respective developments and their implications for Moyn’s analysis. Where Moyn retains an optimism that more humanity could do some good to recover peace, we conclude that once brought into the realm of war, it is humanity that needs to be questioned, perhaps even abandoned.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.036 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".