Pierson vs. Post RevisitedA Reconstruction using the Carneades Argumentation Framework
Why this work is in the frame
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Bibliographic record
Abstract
The Pierson vs. Post case [1] has become an important benchmark in the field of AI and Law for computational models of argumentation. In [2], Bench-Capon used Pierson vs. Post to motivate the use of values and value preferences in his theory-construction account of legal argument. And in a more a recent paper by Atkinson, Bench-Capon and McBurney [3], it was used to illustrate a formalization of an argumentation scheme for practical reasoning. Here we offer yet another reconstruction of Pierson vs. Post, using our Carneades Argumentation Framework, a formal mathematical model of argument structure and evaluation based on Walton's theory of argumentation [4], and compare it to this prior work. Carneades, named in honor of the Greek skeptic philosopher who emphasized the importance of plausible reasoning, applies proof standards [5] to determine the defensibility of arguments and the acceptability of statements on an issue-by-issue basis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it