Bibliographic record
Abstract
One of the most common criticisms made in argument is the reply “That's beside the point!” or “That's irrelevant.” However, relevance is such a broad term that the criticism of being irrelevant could refer to many different kinds of failure or shortcoming in an argument. The study of relevance in argument begins by clarifying and classifying these different types of alleged failure that can prompt the criticism that a breach of relevance has been committed. The primary basis of allegations of irrelevance stems from an important basic feature of all reasonable dialogue. Every argument presupposes a context of dialogue in which there is an issue, or perhaps several issues, being discussed. An issue means there is a proposition or question of controversy under discussion. Typically, an issue in dialogue suggests that there are two sides to the discussion. In other words, there is a certain specific proposition being discussed, and one participant in the dialogue is committed to that proposition being true while the other participant is committed to its being false. Of course, dialogues are not always this clear or simple, but when they are of this form, the type of dialogue may be called a dispute (or disputation ). A dispute is a dialogue where one side affirms a certain proposition, and the other side affirms the opposite (negation) of that proposition.
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 | 0.057 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.084 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.020 | 0.034 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".