Character Attacks as Complex Strategies of Legal Argumentation
Bibliographic record
Abstract
In this paper we analyze leading criminal cases taken from the Supreme Court of the United States, in which ad hominem arguments played a crucial role. We show that although such character attack arguments can be used for legitimate purposes in legal argumentation, in many cases they are weak arguments, but so persuasive that they can effectively prejudice the judgment of a jury. Their dangerous and prejudicial effect can be used as a fundamental component of more complex strategies, aimed, for instance, at shifting the burden of producing evidence or proving character. Using argumentation schemes, we provide criteria for establishing the reasonableness and the weaknesses of this type of argument in different circumstances. We show how ad hominem arguments can be used legitimately as undercutters aimed at undermining the conditions on w hich arguments from a source (such as arguments from expert testimony) are based. We explain the rhetorical persuasiveness of personal attacks by revealing their structure as complex strategies that fit clusters of arguments together to arouse different types of emotions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".