Bibliographic record
Abstract
One of the most valuable uses of schemes is to enable an argument analyst to fill in implicit assumptions needed to make sense of a given argument she is trying to analyze. Arguments that have missing (unstated) premises or conclusions are traditionally called enthymemes in logic. One problem with enthymemes is that reasonable people can have differences of opinion on what the implicit assumptions are supposed to be. Filling in the missing parts of an enthymeme may depend on interpreting the natural language text in which the argument was put forward, to try to fairly judge what the speaker meant to say. The danger of attributing such missing assumptions to an arguer is that of unwittingly committing the straw man fallacy. This fallacy is committed when an arguer misrepresents her opponent's position to make it look more extreme or unreasonable than it really is, in order to attack it more easily. In some cases, more than one interpretation of a given argument is possible. Thus the problem is to find out what kind of evidence is needed to support or question the claim that some proposition really can be inserted into an apparently incomplete argument presented in a text of discourse, without unfairly distorting what the speaker meant to say. It will be shown in this chapter, by studying key examples, how argumentation schemes constitute an important part of this evidence. The goal of this chapter generally is to explore the role of argumentation schemes in enthymeme reconstruction.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".