Bibliographic record
Abstract
Dialogue games provide rules to establish the relationship between a move and a possible effect on commitments. However, such games are highly abstract, and the moves are reduced to logical operations between symbols indicating sentences. The type of speech act performed is not considered, nor is the kind of relation between the predicates in the sentence taken into account. However, a statement affects the commitment store differently from an order or a reminder, and a relation of classification leaves to the interlocutor a range of possible rebuttals different from an analogical relation. In order to apply such games to actual dialogues, we need to go a step further and analyze the nature of the moves and the structure of the sentences. In this chapter we will apply dialogue games to a particular type of move, the act of defining or redefining. As seen in Chapters 3, 4, and 5, definitions can be distinguished according to their pragmatic nature and their propositional structure. In our dialectical approach, definitions can be thought of as moves in a dialogue game, which open different possibilities of continuation of the dialogue and refutation according to the definitional act performed and the type of definitional sentence. For this reason, we will examine the dialectical structures of the different types of definitional sentences and combine them with the commitment effects of the different acts of defining.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".