Bibliographic record
Abstract
The previous chapters have given plenty of good reasons to be suspicious about the use of persuasive definitions and emotive language in argumentation, and to often see them, especially when examining discourse from a logical point of view, as suspicious, or even as inherently illegitimate moves. But is it possible that rational persuasion can be shown to be a legitimate aim of argumentation by providing some kind of objective framework in which there are rules for proper persuasion? Is there a procedural setting in which a persuasion attempt could be an appropriate speech act properly employed so that, under the right conditions, it could be a legitimate move in rational argumentation? In this chapter we show how we need to study how definitions and arguments containing loaded terms are put forth as part of a sequence of argumentation in a dialogue exchange. The move made in a dialogue where a party puts forward an argument, or where a party puts forward a definition that she wants the other party to accept, needs to be seen as a kind of speech act that can only be properly understood in a rule-governed dialogue setting, we will argue. Although there can be different kinds of dialogues, the principal model for evaluating argumentation in cases of the use of emotively loaded language and persuasive definitions is that of the persuasion dialogue, a formal structure with moves and rules, and in which the aim of each participant is rational persuasion based on the values, commitments, and knowledge of the other party. As shown by a thematic example in the chapter, this model enables an analyst to systematically analyze arguments based on persuasive definitions and emotive terms in order to distinguish between cases where such arguments are reasonable and in cases where they are used as fallacious tactics to try to get the best of a speech partner unfairly.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".