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Record W4221034197 · doi:10.22329/il.v42i1.7210

Introduction to the Special Issue

2022· article· en· W4221034197 on OpenAlexvenueno aff
Fabrizio Macagno, Alice Toniolo

Bibliographic record

VenueInformal Logic · 2022
Typearticle
Languageen
FieldComputer Science
TopicMulti-Agent Systems and Negotiation
Canadian institutionsnot available
FundersFundação para a Ciência e a Tecnologia
KeywordsDialogical selfArgumentation theoryEpistemologyInformal logicSociologyComputer scienceEngineering ethicsPhilosophyEngineering

Abstract

fetched live from OpenAlex

Douglas Walton’s work is extremely vast, multifaceted, and interdisciplinary. He developed theoretical proposals that have been used in disciplines that are not traditionally related to philosophy, such as law, education, discourse analysis, artificial intelligence, or medical communication. Through his papers and books, Walton redefined the boundaries not only of argumentation theory, but also logic and philosophy. He was a philosopher in the sense that his interest was developing theoretical models that can help explain reality, and more importantly interact with it. For this reason, he proposed methods that have been used for analyzing different types of dialogical interactions, and modeling procedures for regulating them.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.436
Threshold uncertainty score0.804

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0080.005
Open science0.0020.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.4360.288

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 head0.017
GPT teacher head0.235
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.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations3
Published2022
Admission routes1
Has abstractyes

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