Argument quality: An interdisciplinary perspective
Bibliographic record
Abstract
Argument quality: An interdisciplinary perspective Participants Ulrike Hahn (hahnu@cf.ac.uk) John Woods (jhwoods@interchange.ubc.ca) School of Psychology, Cardiff University, Park Place, Cardiff, CF10 3AT, UK Dept. of Philosophy, University of British Columbia, Vancouver, B.C., V6T 1Z4, Canada Dept. of Computer Science, King’s College London, The Strand, London, WC2R 2LS, UK Frans van Eemeren (F.H.vanEemeren@uva.nl) Dept. of Speech Communication, Argumentation Theory and Rhetoric, University of Amsterdam Amsterdam, 1012 VB, Netherlands John Fox (john.fox@eng.ox.ac.uk) Dept. of Engineering Science, University of Oxford Parks Road, Oxford OX1 3PJ, UK Discussant Mike Oaksford (m.oaksford@bbk.ac.uk) School of Psychology, Birkbeck College, University of London Malet Str., London, WC1E 7HX, UK Keywords: argumentation, argument quality, fallacies of argumentation. success, that is, about what –descriptively- ‘works’ in convincing others of a position. At the same time, however, researchers in all of the above areas are necessarily engaged in the question of what should convince us, and which are the appropriate normative standards against which argument quality should be judged. So-called fallacies of argumentation have had a central role in the question of argument quality. Fallacies are arguments that might seem correct but aren’t, that is, arguments that might persuade but really should not. Well- known examples include circular arguments (“God exists because the Bible says so and the Bible is the word of God”), arguments from ignorance (“Ghosts exist, because no-one has proven that they don’t”), ad hominem arguments or simple appeals to authority. These informal arguments are pervasive in everyday discourse. On a theoretical level, fallacies have been a longstanding focus of debate. Catalogues of reasoning and argumentation fallacies originate with Aristotle and continue to concern philosophers, logicians, and argumentation theorists to this day. The longstanding goal of fallacies research has been to provide a comprehensive treatment of these fallacies that can explain exactly why they are ‘bad’ arguments. In other words, the fallacies are a litmus test for our theories of argument quality. Though seemingly a simple question, it has proven extremely difficult to provide a comprehensive answer to the question of ‘what makes a good argument’. The normative question has attracted the formal tools of logic and, more recently, probability theory, and a pragma- dialectical emphasis on norms underlying argumentative discourse, such as rights to reply, and burdens of proof. Even though the issue of argument quality is prominent within its associated disciplines, the topic has had little presence at meetings of the Cognitive Science Society. The aim of the symposium is to bring the breadth of current interdisciplinary research on this topic to the attention of a cognitive science audience. All speakers are key exponents Introduction Argumentation is central to our complex world, in particular to our social world. It pervades law, politics, academia, and everyday negotiation of what to do and how. Given its centrality, it is not surprising that it is the concern of a wide range of disciplines: philosophy, psychology, education, logic and computer science all have large research programs dealing with argumentation, though they differ in the aspects they emphasize. Philosophers have traditionally focussed on normative theories, that is, theories of how we should behave. The traditional standard here has been formal logic, but more recently, pragma-dialectical theories have focussed on the norms and conventions governing argumentative process as a means of overcoming some of the limitations of logical analysis. Within psychology, ‘persuasion’ has been an important topic of social psychological research. This has led to a vast literature that has identified many of the moderating variables (e.g., speaker likeability, engagement, mode of presentation, fit with prior beliefs) that affect the degree to which a persuasive communication will be effective. Developmental and education research have focused on the way children’s argumentation skills develop, and examined ways in which critical thinking and argument skills might be fostered. Logicians and computer scientists have sought to devise novel frameworks for dealing with dialectical information, seeking to capture the structural relationships between theses, rebuttals, and supporting arguments with the degree of explicitness necessary for the design of computational argumentation system. A shared, focal concern for all of these areas is the issue of argument quality: what makes a good argument, and how can good arguments be distinguished from bad ones? This question has two aspects- one descriptive and one normative. On a descriptive level, this question is about
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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 | 0.002 | 0.012 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".