Bibliographic record
Abstract
Could we enrich speech-act theory to deal with discourse? Wittgenstein and Searle pointed out difficulties. Most conversations lack a conversational purpose, they require collective intentionality, their background is indefinitely open, irrelevant and infelicitous utterances do not prevent conversations to continue, etc. Like Wittgenstein and Searle I am sceptic about the possibility of a general theory of all kinds of language-games. In my view, the single primary purpose of discourse pragmatics is to analyse the structure and dynamics of language-games whose type is provided with an internal conversational goal. Such games are indispensable to any kind of discourse. They have a descriptive, deliberative, declaratory or expressive conversational goal corresponding to a possible direction of fit between words and things. Logic can analyse felicity-conditions of such language-games because they are conducted according to systems of constitutive rules. Speakers often speak non-literally or non-seriously. The real units of conversation are therefore attempted illocutions whether literal, serious or not. I will show how to construct speaker-meaning from sentence-meaning, conversational background and conversational maxims. I agree with Montague that we need the resources of formalisms (proof, model- and game-theories) and of mathematical and philosophical logic in pragmatics. I will explain how to further develop propositional and illocutionary logics, the logic of attitudes and of action in order to characterize our ability to converse. I will also compare my approach to others (Austin, Belnap, Grice, Montague, Searle, Sperber and Wilson, Kamp, Wittgenstein) as regards hypotheses, methodology and other issues.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.012 | 0.028 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".