Bibliographic record
Abstract
Topics ( topoi ), in a long tradition stemming from Aristotle's rhetoric and early writings on argumentation and logic, are the places where arguments can be found to make a case, and the warrants that can back a logical inference leading from premises to a conclusion. Argumentation schemes are tools of modern argumentation theory that have been developed to fulfil the latter function, but may also be useful to fulfill the former one as well. In this chapter we will outline the varied developments of the topoi in both the logical and rhetorical traditions, starting with Aristotle, the first to describe them. We will examine some leading accounts of them given in the Middle Ages, when they were studied in relation to logical consequences. Aristotle's Topics contains accounts of many commonly used types of arguments he calls topics ( topoi , or places). There are some 300–400 of these topics, depending on how you count them, according to Kienpointner (1997, p. 227). Many topics can also be found in Aristotle's Rhetoric . What these topics supposedly represent has been subject to many different interpretations over the centuries. Many have interpreted the topic as a device to help an arguer search around to find a useful argument she can use, for example, in a debate or in a court of law. Other have taken the topic to have a guaranteeing or warranting function that enables rational inferences to be drawn from a set of premises to a conclusion.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".