Bibliographic record
Abstract
Evaluating argumentation in a dialogue model, in which two parties reason with each other, is an old idea that goes back to Aristotle's earlier writings, and even to the sophists. But after the Greeks, the idea lost favor, although it persisted for a time in the scholastic disputations of the middle ages. Aristotle's syllogistic dominated the field of logic for many centuries, until it was superseded by other forms of deductive calculi – propositional and quantifier logics. It was not until the advent of the Erlangen School in Germany that anyone tried to revive the dialogue model and to carry out a systematic program for constructing a system of calculation based on it. But it was not until Hamblin's construction of mathematical models of dialogue (1970, 1971) that a general structure of logical dialogue systems was put forward that was well enough developed to show promise of providing methods for evaluating arguments and fallacies that would hold practical interest for logicians. Alexy (1989) showed how such dialogue systems can be applied to legal argumentation, a program that is now being carried forward by a group of researchers in AI and law including Bench-Capon (1995), Prakken and Sartor (1996, 1998), Verheij (1996, 2000), and Lodder (1998, 1999). This line of research is now often called computational dialectics. It would appear that Gordon invented the term.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".