Bibliographic record
Abstract
Abstract: The concept of argument structure is pivotal in argumentation theory and is extensively employed to analyze and describe arguments. However, as indicated in a previous study (Yu & Zenker, 2022), extant strength-based and relevance-based approaches fall short in distinguishing linked and convergent structures. This paper aims to address this gap by proposing a new argument scheme-based approach and demonstrating its validity. After reviewing the presupposition and inconsistency problems of existing approaches, we analyze their origins in-depth, propose the argument scheme-based approach, demonstrate its validity, and discuss its advantages and challenges. Finally, we argue that our approach, rather than diminishing the concept of argument structure, restores it to its rightful theoretical position. Résumé: Le concept de structure argumentative est essentiel en théorie de l'argumentation et il est largement utilisé pour analyser et décrire les arguments. Cependant, comme indiqué dans une étude précédente (Yu & Zenker, 2022), les approches existantes basées sur la force et la pertinence ne parviennent pas à distinguer les structures liées et convergentes. Cet article vise à combler cette lacune en proposant une nouvelle approche basée sur les schémas argumentatifs et en démontrant sa validité. Après avoir examiné les problèmes de présupposition et d'incohérence des approches existantes, nous analysons leurs origines en profondeur, proposons l'approche basée sur les schémas argumentatifs, démontrons sa validité et discutons de ses avantages et de ses défis. Enfin, nous soutenons que notre approche, plutôt que de diminuer le concept de structure argumentative, lui redonne sa place théorique légitime.
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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".