Bibliographic record
Abstract
Dialogues, as we saw in chapter 1, have characteristics such as civility, meaning that the two participants take turns making various moves. This chapter begins by analyzing the different types of dialogue that embody such characteristics in order to make the dialogue successful as an environment for using rational argumentation. Such moves include not only the putting forward of arguments, but also the asking of questions, including critical questions used to respond to arguments. It is the connected sequence of questions and answers, as well as chains of arguments, that make up the dialogues. Thus, asking the right questions in a dialogue and responding appropriately to the other party's questions are important aspects of what makes a dialogue move forward. This chapter classifies the different types of dialogue and examines some of the main properties of questions and how they are used in dialogues. Questioning is obviously very important in law and politics. For example, in a trial, a lawyer has to question a witness and sometimes in cross-examination can do so in quite a probing, even aggressive way. The lawyer for the other side often needs to object to such questions. Questioning is also very important in science at the discovery stage, where hypotheses are formulated. Asking questions often seems like an innocent and harmless enough activity, you might think, from a viewpoint of critical argumentation.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.085 | 0.024 |
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".