Bibliographic record
Abstract
In chapter 5 it was shown how replies to questions can be irrelevant, but there is a still more general problem about relevance concerning argumentation. How can arguments, or any moves in a dialogue, for that matter, be judged relevant or irrelevant? This problem is a central one for critical argumentation because many of the emotional appeals commonly used in argumentation, such as appeal to pity or fear or ad hominem arguments, are fallacious because they are irrelevant arguments. They are powerful tactics of distraction that work to throw an arguer off the trail, creating distractions and confusion by arousing powerful emotions. However, appeals to emotion are not always fallacious. Sometimes they are relevant. So there is a problem of judging in any given case when such an appeal should be considered relevant or not. While argumentation schemes are helpful for this purpose, judging relevance often means one also has to examine a more lengthy chain of argumentation in a dialogue. As indicated in chapter 1, a sequence of argumentation in a dialogue should always have a particular proposition it is ultimately aimed to prove as its target. Its target is the issue that the dialogue is supposed to settle. In a critical discussion, the chain of argumentation is aimed at proving or casting doubt on some particular proposition at issue in a dialogue.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".