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Record W2471432156 · doi:10.12775/llp.2016.014

Profiles of Dialogue for Repairing Faults in Arguments from Expert Opinion

2016· article· en· W2471432156 on OpenAlexafffund
Marcin Koszowy, Douglas Walton

Bibliographic record

VenueLogic and Logical Philosophy · 2016
Typearticle
Languageen
FieldComputer Science
TopicMulti-Agent Systems and Negotiation
Canadian institutionsUniversity of Windsor
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFallacyArgument (complex analysis)Expert opinionComputer scienceSoftwareGraphIntellectual propertyProperty (philosophy)Natural language processingArtificial intelligenceLinguisticsEpistemologyPhilosophyTheoretical computer scienceProgramming language

Abstract

fetched live from OpenAlex

Using the profiles of dialogue method we identify a species of ad verecundiam fallacy that works by forestalling of questioning in arguments from expert opinion. A profile of dialogue is a graph structure used to model a sequence of speech acts surrounding both the putting forward of an argument and the response to it at the next moves in a dialogue. The method is applied to a case of cross-examining a software engineer in a legal deposition in a case of intellectual property litigation.

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)0.025
metaresearch head score (Gemma)0.107
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.107
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.003
Science and technology studies0.0030.006
Scholarly communication0.0060.017
Open science0.0040.008
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0060.002

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 head0.072
GPT teacher head0.290
Teacher spread0.218 · 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 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

Citations9
Published2016
Admission routes2
Has abstractyes

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