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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".