Bibliographic record
Abstract
1. My book defends an account of argument cogency according to which a person P ought to be persuaded by an argument A just in case it’s rational for P to believe that (i) each of A’s premises is true, (ii) A’s premise set S is relevant to A’s conclusion, (iii) S provides enough evidence to justify belief in (i.e. to ground) A’s conclusion, and (iv) A is compact. Clause (ii) is redundant in the sense that any argument that satisfies the third (or G condition) will trivially satisfy the second (or R) condition as well. So, Goddu asks, why bother with relevance as a separate condition of cogency (IL, p. 297)? Relevance can seem unimportant if we focus exclusively on argumentative success. If we understand why it’s rational for P to believe that S grounds A’s conclusion, then it’s pointless to inquire separately whether it’s rational for P to believe that S is relevant to that conclusion. But if we’re also interested in understanding argumentative failure—the various ways in which and reasons why arguments fail to be cogent— then there’s a world of difference between an argument that fails the G condition because it fails the R condition, and an argument that fails the G condition despite the fact that it passes the R condition. Some arguments fail to be cogent because they appeal to irrelevant information. Other arguments fail to be cogent because they appeal to information that is relevant but not substantial enough to justify belief in the conclusion. There’s no way of marking this important distinction without invoking (something like) the R condition.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".