Bibliographic record
Abstract
Fallacies of relevance, a major category of informal fallacies, include two that could be called pure fallacies of relevance-the wrong conclusion (ignoratio elenchi, wrong conclusion, missing the point) fallacy and the red herring digression, diversion) fallacy. The problem is how to classify examples of these fallacies so that they clearly fall into the one category or the other, on some rational system of classification. In this paper, the argument diagramming software system, Araucaria. is used to analyze the argumentation in some selected textbook examples of pure fallacies of relevance. A system of classification of these fallacies is proposed, and criteria for determining whether an example should be classified as wrong conclusion or red herring are formulated. A key difference cited is that in a case where the red herring fallacy has been committed, even if the argument may go to a wrong conclusion, there is evidence of the use ofa deceptive tactic of diversion. Textual evidence must indicate that the arguer deliberately interjects a distracting controversy to lead the respondent away from the real issue to be disputed.
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 | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".