Bibliographic record
Abstract
The aim of this chapter is to clarify a group of related terms, including ‘argument attack’, ‘rebuttal’, ‘refutation’, ‘challenge’, ‘critical question’, ‘defeater’, ‘undercutting defeater’, ‘rebutting defeater’, ‘exception’ and ‘objection’, which are commonly used in the literature on argumentation. The term ‘rebuttal’ is often associated with the work of Toulmin (1958), while the terms ‘undercutting defeater’ and ‘rebutting defeater’ are associated with the work of Pollock (1995) and are commonly used in the artificial intelligence literature. The notions of argument attack and argument defeat are associated with a formal model of argumentation that is prominent in artificial intelligence called the abstract argumentation framework. As shown in the chapter, these terms are, at their present state of usage, not precise or consistent enough for us to helpfully differentiate their meanings in framing useful advice on how to attack and refute arguments. An additional difficulty is that argument diagramming tools are of limited use if they cannot represent the critical questions matching an argumentation scheme. A way of overcoming both difficulties is presented in this chapter is by using the Carneades Argumentation System.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".