Bibliographic record
Abstract
Argumentation schemes are structures or templates for various kinds of arguments. The argumentation scheme classification system that we present in this paper intro-duces a new task in this field. To the best of our knowledge, this is the first attempt to classify arguments into argumentation schemes automatically. Given the text of an argument with premises and conclusion identified, we clas-sify it as an instance of one of five common schemes, using general features and other features specific to each scheme, including lexical, syntactic, and shallow semantic fea-tures. We achieve accuracies of 63–91 % in one-against-others classification and 80–94% in pairwise classification (baseline = 50 % in both cases). We design a pipeline framework whose ultimate goal is to reconstruct the implicit premises in an argument, and our argumentation scheme classification system is aimed to address the third component in this framework. While the first two portions of this framework can be fulfilled by work of other researchers, we propose a syntactic-based approach to the last component of this framework. The completion of the entire system
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".