Pierson vs. Post RevisitedA Reconstruction using the Carneades Argumentation Framework
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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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".