Bibliographic record
Abstract
Argumentation, which can be abstractly defined as the interaction of different arguments for and against some conclusion, is an important skill to learn for everyday life, law, science, politics and business. It is a rich, interdisciplinary area of research straddling philosophy, communication studies, linguistics, psychology and artificial intelligence that has developed context-sensitive practical methods to help a user identify, analyze and evaluate arguments. Recently, the field of computing has embraced argumentation as a paradigm for research in artificial intelligence and multi-agent systems. Artificial intelligence in particular has seen a prolific growth in uses of argumentation. Argumentation has proved helpful to computing because it has provided concepts and methods used to build software tools for designing, implementing and analyzing sophisticated forms of reasoning and interaction among rational agents (Reed and Grasso, 2007). Recent successes include argumentation-based models of evidential relations and legal processes of examination and evaluation of evidence. Argument mapping has proved to be a useful tool for designing better products and services and for improving the quality of communication in social media by making deliberation dialogues more efficient. There now exist formal systems of argumentation to model many aspects of reasoning and argument that were formerly studied only by less structured methods of informal logic.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.052 | 0.034 |
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".