Bibliographic record
Abstract
Chapter 1 portrayed appeal to witness testimony as a distinctive argumentation scheme with a matching set of critical questions. This approach implies that any given instance of an appeal to witness testimony in a trial needs to be evaluated in the context of a dialogue, in line with the goal appropriate for that type of dialogue. Chapter 4 outlined several different abstract models of dialogue that have been identified in the literature on argumentation theory and computing. Chapter 5 outlined the characteristics of one particular type of dialogue called peirastic examination dialogue that is new on the scene and has been very little investigated in the literature. The most visible and best established instance of this type of dialogue is found in the examination procedure used in our legal system to question witnesses and other participants in a trial. In Sections 6 and 7 of Chapter 5, the abstract model of peirastic examination dialogue was illustrated by features of examination in a trial setting. Now a large question is raised: How can we apply these abstract dialectical models to the existing institution called the trial in law? What sort of dialogue provides the right framework for making witness testimony a form of evidence in a trial? In this chapter we will concentrate on the adversarial theory, embodied in Anglo-American law, where the opposed advocacy arguments of both sides offer the trier a basis for judging which side has the stronger argument, or a strong enough argument to meet the requirements of proof.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".