Commentary on: James B. Freeman's "What types of arguments are there?"
Bibliographic record
Abstract
James Freeman proposes to classify arguments on two dimensions. An argument is “defeasible” if its warrant admits of exceptions, “conclusive” otherwise. It is “a posteriori” if its warrant has to be backed by sense experience, “a priori” otherwise. Freeman argues that all four theoretically possible combinations are actualized. In particular, there are defeasible a priori arguments; the generalizations associated with their warrants are synthetic a priori judgments of the sort recognized by Kant. And there are conclusive a posteriori arguments, whose warrants are universal but based on sense experience. The point of Freeman’s classification is to provide a framework for assessing what he calls, following Govier, the “ground adequacy” of an argument: whether its supporting reasons, if they are acceptable, are jointly sufficient to justify acceptance of its conclusion. Exceptions to an argument’s warrant undermine ground adequacy if the argument is conclusive but not if it is defeasible. Analogously, attention to the extent and variety of observations supporting an argument’s warrant is appropriate for assessing ground adequacy if the argument is a posteriori, but not if it is a priori. The classification scheme thus implies and reflects a pluralistic approach to assessing ground adequacy. 2. TYPES OF WHAT? There is much to agree with, and to reinforce, in Freeman’s proposal. But there is one major respect in which the proposal is misdirected. Freeman is really classifying ways in which reasons can provide adequate grounds for a claim—if you like, kinds of ground adequacy. His taxonomy does not cover arguments in which the reasons are inadequate. Further, arguments generally do not come with their warrant preidentified. The warrant must usually be elicited, with a question like “How does that follow?” And in general more than one answer to that question is defensible. Hence, we cannot classify arguments by their warrants. Further, arguments generally do not carry on their face an indication of whether their reasons support their claim conclusively or defeasibly. One might be tempted, however, to use Freeman’s classification scheme, or any taxonomy of ways a conclusion can follow from given reasons, to classify
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.040 | 0.059 |
| Insufficient payload (model declined to judge) | 0.013 | 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".