Bibliographic record
Abstract
This paper takes on the task of providing a formal system of dialogue CE in which the speech acts of requesting and pro-viding an explanation are represented as dialogue moves in the system. CE has opening rules, locution rules, dialogue rules, success rules and closing rules. The system is meant to be simple and basic, to provide a platform for develop-ing more specialized formal dialogue systems of explanation used for specific purposes. The dialogical theory of explana-tion postulates that an explanation is a dialogue between two parties, one of whom asks a question requesting understand-ing of something which he or she claims not to understand, while the other offers a response that claims to convey the requested understanding to the party asking the question. In the last half of the twentieth century, the dominant model of explanation was the covering law (deductive-nomological) model associated with Hempel (1965), its chief advocate. This model took an explanation to be a de-ductive inference from a set of facts called initial conditions and a set of general rules to a proposition to be explained. It would have been anathema to the analytical philosophers who accepted this model to suggest that an explanation should be thought of as a dialogue between two parties. Times have changed. Much recent work in AI has been based the dialogue model of explanation. Cawsey’s work (1992) on computational generation of explanatory dia-logue used an interactive or dialogue approach, and Moore’s dialogue-based analysis of explanation for advice-giving in expert systems (1995) can also be cited. According to Moore (1995, p. 1) explanation is “an inherently incremental and interactive process ” that requires a dialogue between an ex-planation presenter who is trying to explain something and a questioner who has asked for an explanation. The dialog-ical model of explanation has also been advocated and de-veloped by Schank and his colleagues in cognitive science
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".