Bibliographic record
Abstract
The three goals of critical argumentation are to identify, analyze, and evaluate arguments. The term “argument” is used in a special sense, referring to the giving of reasons to support or criticize a claim that is questionable, or open to doubt. To say something is a successful argument in this sense means that it gives a good reason, or several reasons, to support or criticize a claim. But why should one ever have to give a reason to support a claim? One might have to because the claim is open to doubt. This observation implies that there are always two sides to an argument, and thus that an argument takes the form of a dialogue. On the one side, the argument is put forward as a reason in support of a claim. On the other side, that claim is seen as open to doubt, and the reason for giving the reason is to remove that doubt. In other words, the offering of an argument presupposes a dialogue between two sides. The notion of an argument is best elucidated in terms of its purpose when used in a dialogue. At risk of repetition, the following general statement about arguments is worth remembering throughout chapter 1 and the rest of this book. The basic purpose of offering an argument is to give a reason (or more than one) to support a claim that is subject to doubt, and thereby remove that doubt.
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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 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".