Analogical Arguments in Persuasive and Deliberative Contexts
Bibliographic record
Abstract
This paper uses argumentation tools such as argument diagrams and argumentation schemes to analyze four examples of argument from analogy, and argues that to proceed from there to evaluating these arguments, features of the context of dialogue need to be taken into account. The evidence drawn from these examples is taken to support a pragmatic approach to studying argument from analogy, meaning that identifying the logical form of the argument by building an argument diagram of the premises and conclusion is not by itself sufficient for argument evaluation. To get further, it is argued, the argument evaluator needs to take into account how this particular argument was used in context to support a conversational goal.Cet article utilise des outils d'argumentation tels que des diagrammes d'argument et des schèmes d'argumentation pour analyser trois exemples d'argument par analogie, et soutient que pour évaluer ces arguments de manière adéquate, il est nécessaire de tenir compte du contexte d'utilisation de l'argument. Ces exemples suggèrent que l’étude des arguments par analogie à partir de seulement l’identification de leur forme logique (par exemple en construisant un diagramme des prémisses et de leur conclusion) n'est pas adéquate. Pour aller plus loin, on avance que l'analyste d'argument doit prendre en compte comment un argument particulier a été utilisé dans un contexte pour soutenir un but conversationnel.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".