Bibliographic record
Abstract
Previous chapters have shown that formal dialogue models of argumentation, along with tools from AI like the Carneades Argumentation System and the abstract argumentation framework, apply very well to modeling burden of proof and presumption in the well-organized, rule-directed framework of a legal trial. The objection posed in Chapter 1 was that the legal concepts of burden of proof and presumption have been illicitly transferred from the legal setting to public policy discussions and other arenas where the argumentation is not structured in the same way it is in a legal setting. Chapter 8 takes up this challenge by arguing that the formal dialectical framework of Chapter 4 can be usefully applied to modeling burden of proof and presumption in these other settings. This argument, of course, does not claim the burden of proof and presumption work in every respect in the same way they work in other settings. It only means that the methods used to reason about evidence in the common law system presents an outline of reasonable but defeasible argumentation that has some important features, represented in the dialogue models of burden of proof and presumptions presented in the previous chapters of this book, and these features can be adapted to and applied in a helpful manner to other settings of argumentation outside law. Of course there are many different settings in which argumentation is used as a means of proving a hypothesis, settling a conflict of opinions based on evidence brought forward or arriving at a rational decision on how to make a choice in a deliberation on what to do in a situation requiring such a choice, as indicated in Chapter 7. For the purposes of this book, however, there are two main settings that seem to be of main interest in relation to the issue of transferability of the legal notions of presumption and burden of proof. One is the kind of organized disputation of the kind represented in a forensic debate, or to cite a specific example, a presidential debate that has a moderator and is televised to a large audience.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.238 | 0.088 |
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".