Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".