Artificial persons and attributed actions: How to interpret action-sentences about states
Bibliographic record
Abstract
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.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.008 | 0.025 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".