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Theorizing the Laws of War

2016· book-chapter· en· W2568457769 on OpenAlexaff
Frédéric Megret

Bibliographic record

VenueOxford University Press eBooks · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsMcGill University
Fundersnot available
KeywordsLawObject (grammar)Law of warFunction (biology)Spanish Civil WarPolitical scienceSociologyEpistemologyInternational lawComputer sciencePhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This chapter suggests two predominant modes of theorizing about the laws of war—one ‘internal’, the other ‘external’—both providing a useful shorthand for two relatively irreducible types of exercises. Internal theorizing makes sense of the discipline among its practitioners and within bounds that are taken for granted. It is minimal in that its ambition is largely instrumental: providing the practitioners of the laws of war with the background necessary for them to function. External theorizing is less interested in the laws of war as a system than as an object; it is less focused on explaining the operation of the laws of war than understanding what the laws of war mean generally and for international law specifically. It is more explicitly theoretical precisely in that it seeks to highlight some of the ultimate functioning or purpose of the laws of war behind its dominant implicit theories.

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)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.784
Threshold uncertainty score0.410

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.232
Teacher spread0.206 · 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
GenreOther

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

Citations14
Published2016
Admission routes1
Has abstractyes

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