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Record W4313445350 · doi:10.1007/978-94-6265-559-1_5

Wars with and for Humanity

2023· book-chapter· en· W4313445350 on OpenAlexaff
Craig Jones, Nisha Shah

Bibliographic record

VenueYearbook of international humanitarian law · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanityPolitical scienceLawLaw and economicsSociology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.701
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.299
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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