Bibliographic record
Abstract
Most of the literature on burden of proof in argumentation studies and AI has concentrated so far on the persuasion type of dialogue. This concentration is natural enough, because the bulk of this literature has concentrated on burden of proof in legal argumentation. The most significant exception is deliberation dialogue, where some recent work has begun to tentatively investigate burden of proof in that setting. The problem now posed is whether burden of proof operates in deliberation dialogue in the same way that it operates in persuasion dialogue, or whether there are essential differences in this regard between the two types of dialogue. This chapter analyzes four examples of deliberation dialogue where burden of proof poses a problem. Based on analysis of the argumentation in these examples, a working hypothesis is put forward. It is that burden of proof only becomes relevant when deliberation dialogue shifts, at the beginning of the argumentation stage, to a persuasion dialogue. The hypothesis is that the shift can be classified as embedding one type of dialogue into another, meaning that the goal of the first type of dialogue continues to be supported once the transition to the second type of dialogue has been made (Walton and Krabbe, 1995, 102). In other instances, it is well known that a shift can be illicit, where the advent of the second dialogue interferes with the fulfillment of the goal of the first one. It has also been shown that such shifts can be associated with fallacies, as well as other logical and communicative problems (Walton, 2007, chapter 6).
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".