Bibliographic record
Abstract
Abstract. In this paper a representative example is chosen that is meant be fairly simple for illustrating the point that in a very com-mon kind of instance, argument and explanation are mixed in together in a text of discourse. The example is a short text found on the Inter-net that explains to the reader how to attach a flagpole bracket to the vinyl siding on the side of your house. The example uses practical rea-soning (goal-directed reasoning) of a kind widely studied in AI and logic. While the text appears to be mainly a “how-to ” explanation, it also con-tains argumentation woven into it, as shown by applying argumentation schemes (defeasible argument structures) representing common forms of argument. The problem is one of distinguishing between explanation and argument. 1 The Nature of the Problem One of the most elementary problems in defining the concept of an explanation is to provide criteria to distinguish clearly (in particular cases) the difference
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".