Bibliographic record
Abstract
The Four Cogency Conditions In offering an argument, an author aims to achieve rational persuasion. A cogent argument, we'll say, is an argument by which you ought to be persuaded. More precisely, an argument A is cogent for some person P , within some context C , just in case it is rational for P , within C , to be persuaded to believe the conclusion of A , on the basis of the evidence cited within A 's premises. An argument is non-cogent , for a particular person within a particular context, just in case it is not cogent, within that context, for that person, i.e., just in case that person should not be persuaded by the argument in question. In this chapter, we'll discuss four conditions that are individually necessary and jointly sufficient for argument cogency. This discussion will also allow us later, in Chapter 3, to clarify the notion of argument strength that we employed at an intuitive level throughout Chapter 1. The four components of argument cogency are designed to delineate the conditions under which it is rational for someone to adopt a new belief, within an argumentative setting. Cogency is a person-relative property of arguments, since whether it's rational for someone to adopt a belief, on the basis of certain evidence, will often depend upon what else that person already rationally believes, and sometimes (perhaps less obviously) upon other features of her subjective standpoint.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".