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Record W2557603865 · doi:10.1177/1354066116679244

Artificial persons and attributed actions: How to interpret action-sentences about states

2016· article· en· W2557603865 on OpenAlexfundno aff
Sean Fleming

Bibliographic record

VenueEuropean Journal of International Relations · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicSeventeenth-Century Political and Philosophical Thought
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInterpretation (philosophy)Action (physics)Argument (complex analysis)AttributiveLiteral and figurative languageLinguisticsTypologyState (computer science)EpistemologyPsychologySociologyComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Action-sentences about states, such as ‘North Korea conducted a nuclear test’, are ubiquitous in discourse about international relations. Although there has been a great deal of debate in International Relations about whether states are agents or actors, the question of how to interpret action-sentences about states has been treated as secondary or epiphenomenal. This article focuses on our practices of speaking and writing about the state rather than the ontology of the state. It uses Hobbes’ theory of attributed action to develop a typology of action-sentences and to analyse action-sentences about states. These sentences are not shorthand for action-sentences about individuals, as proponents of the metaphorical interpretation suggest. Nor do they describe the actions of singular agents, as proponents of the literal interpretation suggest. The central argument is that action-sentences about states are ‘attributive’, much like sentences about principals who act vicariously through agents: they identify the ‘owners’ of actions — the entities that are responsible for them — rather than the agents that perform the actions. Our practice of ascribing actions to states is not merely figurative, but nor does it presuppose that states are corporate agents.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.877
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.100
GPT teacher head0.293
Teacher spread0.193 · 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.

Study designNot applicable
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

Citations6
Published2016
Admission routes1
Has abstractyes

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