Is Deterrence Morally and Legally Permissible and Is It a Form of State Terrorism?
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".