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Record W2905583051 · doi:10.1007/978-94-6265-267-5_14

Is Deterrence Morally and Legally Permissible and Is It a Form of State Terrorism?

2018· book-chapter· en· W2905583051 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueT.M.C. Asser Press eBooks · 2018
Typebook-chapter
Languageen
FieldSocial Sciences
TopicNuclear Issues and Defense
Canadian institutionsUniversity of Manitoba
FundersUniversity of Manitoba
KeywordsTerrorismDeterrence theoryPrinciple of legalityPolitical scienceState (computer science)Deterrence (psychology)Law and economicsLawNuclear weaponNuclear ethicsCriminologySociology

Abstract

fetched live from OpenAlex

This chapter examines the recent nuclear threatsNuclear threats made between US President Donald Trump and leader of North Korea Kim Jong Un in 2017 and compares them with traditional strategies of deterrence that emerged in World War II and the Cold War and argues that these threats are a form of nuclear deterrenceNuclear deterrence which involve threats to kill innocent civilians with nuclear weapons. First, I define deterrence and argue that the threats of Trump and Kim fit this definition. Next, I present moral arguments for deterrence and my objections to those arguments. Then, I present arguments against deterrence and answer potential objections to those arguments. Next, I examine the legality of the Trump/Kim form of deterrence. Finally, I define terrorism and point out the similarities between the Trump/Kim form of deterrence and terrorist tactics. I conclude that this kind of deterrence is not morally permissible, potentially illegal, and can be seen as a form of state terrorism.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.831
Threshold uncertainty score1.000

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)0.0010.000

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 head0.080
GPT teacher head0.332
Teacher spread0.252 · 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