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Record W2772219500 · doi:10.2966/scrip.140217.168

Argument Invention with the Carneades Argumentation System

2017· article· en· W2772219500 on OpenAlexafffund
Douglas Walton, Thomas F. Gordon

Bibliographic record

VenueSCRIPTed A Journal of Law Technology & Society · 2017
Typearticle
Languageen
FieldComputer Science
TopicMulti-Agent Systems and Negotiation
Canadian institutionsUniversity of Windsor
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsArgumentation theoryArgument (complex analysis)Argument mapRhetorical questionRhetoricWatsonEpistemologyComputer scienceIBMPhilosophyArtificial intelligenceLinguistics

Abstract

fetched live from OpenAlex

Argument invention (inventio) has traditionally been regarded as one of the five main components of rhetoric, but has remained an ambiguous, vague and highly contested concept, made even more confusing by its dependence on the Aristotelian topics, supposedly the places in which the rhetorical persuader can find arguments useful to support or attack a claim. The advent of two recently developed computational tools for argument invention, the Carneades Argumentation System and IBM’s Watson Debater tool, calls for a rethinking of the notion of argument invention in line with the state of the art of formal and computational argumentation systems in artificial intelligence. The role of argumentation schemes is an important part of this investigation into argument invention.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0040.005
Scholarly communication0.0090.008
Open science0.0030.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0180.004

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 head0.021
GPT teacher head0.258
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations14
Published2017
Admission routes2
Has abstractyes

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Same venueSCRIPTed A Journal of Law Technology & SocietySame topicMulti-Agent Systems and NegotiationFrench-language works237,207