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Record W99876286

The Hidden Role of Pathos in Toulmin’s Layout of Argument

2005· article· en· W99876286 on OpenAlexaff
Jean Nienkamp

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2005
Typearticle
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPathosArgumentation theoryArgument (complex analysis)EpistemologyRhetorical questionRationalityAppealAppeal to emotionSociologyPhilosophyPsychologyLawLinguisticsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Stephen Toulmin’s use of a judicial model for argumentation in The Uses of Argument means that he is introducing the complexity of rhetorical appeals to the hitherto logic-based study of argumentation, including the appeal to the emotions, pathos. Toulmin’s acknowledgment of the role of the emotions in practical reasoning moves from being implicit in The Uses of Argument to becoming more explicit in Toulmin’s Return to Reason: ‘Warm hearts allied with cool heads seek a middle way between the extremes of abstract theory and personal impulse’ (2001, p. 214). This paper analyzes the hidden role of pathos in Toulmin’s distinction between rationality and reasonableness, particularly as it appears in Cosmopolis and his later works. To Toulmin’s characterization of the oral, particular, local, and timely nature of reasonableness, I add Peter Goldie’s notions of intelligibility, appropriateness, and proportionality of emotions to describe what role emotions play in reasonable argumentation. Using as a case study the victim impact testimony in Timothy McVeigh’s Oklahoma City bombing trial, I argue that in certain situations and fields of argumentation, pathos—or data with a high emotional content—is warranted in a reasonable argument, and that it would be unreasonable to exclude such data.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.192
Teacher spread0.185 · 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 designObservational
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
Published2005
Admission routes1
Has abstractyes

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