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Record W1609079276 · doi:10.22329/il.v28i3.536

Cogency, Compactness and Microstructure

2009· article· en· W1609079276 on OpenAlexaffvenue
Mark Vorobej

Bibliographic record

VenueInformal Logic · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicEpistemology, Ethics, and Metaphysics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAppealArgumentativeArgument (complex analysis)PremiseRelevance (law)EpistemologyPhilosophySet (abstract data type)Property (philosophy)LawPolitical scienceComputer science

Abstract

fetched live from OpenAlex

1. My book defends an account of argument cogency according to which a person P ought to be persuaded by an argument A just in case it’s rational for P to believe that (i) each of A’s premises is true, (ii) A’s premise set S is relevant to A’s conclusion, (iii) S provides enough evidence to justify belief in (i.e. to ground) A’s conclusion, and (iv) A is compact. Clause (ii) is redundant in the sense that any argument that satisfies the third (or G condition) will trivially satisfy the second (or R) condition as well. So, Goddu asks, why bother with relevance as a separate condition of cogency (IL, p. 297)? Relevance can seem unimportant if we focus exclusively on argumentative success. If we understand why it’s rational for P to believe that S grounds A’s conclusion, then it’s pointless to inquire separately whether it’s rational for P to believe that S is relevant to that conclusion. But if we’re also interested in understanding argumentative failure—the various ways in which and reasons why arguments fail to be cogent— then there’s a world of difference between an argument that fails the G condition because it fails the R condition, and an argument that fails the G condition despite the fact that it passes the R condition. Some arguments fail to be cogent because they appeal to irrelevant information. Other arguments fail to be cogent because they appeal to information that is relevant but not substantial enough to justify belief in the conclusion. There’s no way of marking this important distinction without invoking (something like) the R condition.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.332

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.049
GPT teacher head0.264
Teacher spread0.215 · 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
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

Citations0
Published2009
Admission routes2
Has abstractyes

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