Bibliographic record
Abstract
We are all familiar with the traditional conception of what makes for a good argument: that its premises are true and that it is valid.[1] That traditional view of the goodness of arguments has sustained serious criticism over the past few decades, so that most theorists have dropped either the validity requirement or the truth requirement or both. Almost all theorists that I am aware of take it that an argument is good if it fulfills its purpose, and it is widely agreed that arguments can fulfill their purpose even when not all of their premises are true, or they are not deductively valid. Still, some theorists retain a focus on the truth-directed nature of arguments, and those theorists rightly hold that, given such a focus, truth plays an important role in the evaluation of arguments. Johnson (2000) goes so far as to reintroduce truth as a constraint on premise adequacy, alongside the criterion of acceptability, and he holds that in a case where a premise is false but acceptable, truth (/falsity) outweighs acceptability. What I propose to do in this essay is to discuss the role that truth plays in the evaluation of arguments, when the purpose of arguments is understood as truth-directed in some important way. I begin with a discussion of truth and the purpose of arguments. In the second section of the paper, I give an argument to the effect that the theory of argument evaluation ought not to involve truth as a constraint on premise adequacy. The third section contains my argument for the positive claim that the proper place for the concept of truth is in the metatheory in terms of which the theory of evaluation is worked out.[2] I conclude the essay with a response to Hamblin’s (1970) argument that no arguments are truth-directed. [1] Hitchcock (1999) traces the concept of soundness in the textbook tradition to Black (1946), and before that (but with different terminology), to Cohen and Nagel (1934). [2] By "theory of evaluation" I mean the set of criteria that a theory provides us with for evaluating arguments. By "the metatheory" in terms of which the theory of evaluation is worked out, I mean the broader theory of argument, including reference to what it is that the criteria for argument evaluation are supposed to accomplish, in which the theory of evaluation is articulated.
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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 | 0.024 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 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".