Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dung intended his abstract argument frameworks to be used for modeling a particular form of human argumentation, where arguments attack each other and are evaluated following the principle summarized by “The one who has the last word laughs best.” However this form does not fit a wide class of arguments, which is arguably more prototypical and common in human argumentation, namely arguments where pros and cons are balanced to choose among alternative options. Here we present a formal model of structured argument which generalizes Dung abstract argumentation frameworks to also handle balancing. Unlike most other models of structured argument, this model does not map structured arguments to abstract arguments. Rather it generalizes abstract argumentation frameworks, allowing them to be simulated using structured arguments. The model can handle cumulative arguments (“accrual”) without causing an exponential blowup in the number of arguments and has been fully implemented in Version 4 of the Carneades Argumentation System.
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.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it