Bibliographic record
Abstract
Could we enrich speech-act theory to deal with discourse? Wittgenstein and Searle are sceptical. In my view, the primary aim of discourse pragmatics is to analyze the structure and dynamics of language-games with an internal conversational goal. Logic can analyze felicity-conditions of such collective illocutions. For interlocutors obey systems of constitutive rules in conducting descriptive, deliberative, declaratory or expressive dialogues. I will show how to construct speaker-meaning from sentence-meaning, conversational background and maxims. I will also explain how to use the resources of formalisms and mathematical logic and to further develop intensional and illocutionary logics, the logic of attitudes and of action in order to characterize our ability to converse. I will also deal with the nature of intelligent dialogues between man and machines in A.I.
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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.009 | 0.025 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".