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Record W3011596214

How States Persuade: An Account of International Legal Argument Upon the Use of Force

2020· article· en· W3011596214 on OpenAlexaff
David Hughes

Bibliographic record

VenueSSRN Electronic Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCybersecurity and Cyber Warfare Studies
Canadian institutionsYork UniversityUniversity of Ottawa
Fundersnot available
KeywordsPersuasionArgument (complex analysis)Prima facieInternational lawConversationLaw and economicsPolitical scienceLawCompliance (psychology)State (computer science)SociologySocial psychologyPsychologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

This Article presents a theory of the sociology of argument in international law and considers how persuasion, through international legal argument, shapes legal change and influences notions of compliance. Commonly, international law is portrayed as a medium for debate. Within the resulting debates, persuasion is understood as a tool to induce compliance. Yet this is only one side of the conversation. Persuasion is a two-way discourse. Efforts to alter the behavior of a “non-compliant” state through cogent communication are often met with or preempted by legal arguments put forth by the state. This is perhaps most apparent in the deliberative environments that accompany the use of force and the conduct of warfare. Built around a series of case studies in which states offer legal arguments in support of actions that, prima facie, extend beyond the limits of legal permissibility, this Article presents a theory of persuasion and legal communication that differs from how legal argument and international law are commonly under-stood. This Article offers a detailed and theorized account of the processes through which the non-compliant state argues, persuades, and employs international law. By mapping and conceptualizing persuasive techniques, I suggest that international law must be considered both in compliance and in violation. Switching emphasis and considering the actions and arguments offered by the “non-compliant” state facilitates a novel and complete understanding of the diplomatic, informal, and daily interactions that more commonly and more consequentially define how international law is understood, practiced, and altered.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.284
Teacher spread0.254 · 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
GenreEmpirical

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

Citations0
Published2020
Admission routes1
Has abstractyes

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