Advances in the Theory of Argumentation Schemes and Critical Questions
Bibliographic record
Abstract
This paper begins a working through of Blair’s (2001) theoretical agenda concerning argumentation schemes and their attendant critical questions, in which we propose a number of solutions to some outstanding theoretical issues. We consider the classification of schemes, their ultimate nature, their role in argument reconstruction, their foundation as normative categories of argument, and the evaluative role of critical questions.We demonstrate the role of schemes in argument reconstruction, and defend a normative account of their nature against specific criticisms due to Pinto (2001). Concerning critical questions, we propose an account on which they are founded in the R.S.A. cogency standard, and develop an account of the relationship between critical questions and burden of proof. Our ultimate aim is to initiate a reconciliation between dialectical and informal logic approaches to the schemes.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.011 | 0.032 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".