Bibliographic record
Abstract
In combining a pluri‐propositionalist framework (Bach‐style) concerning alleged conventional implicatures, and a pluri‐propositionalist framework (Perry‐style) distinguishing various levels of content associated with a single utterance, I defend a Grice‐inspired model of communication. In so doing, I rely on the distinction between what is said, i.e. what is semantically encoded, and what is pragmatically implicated. I show how the notion of same‐saying plays a central role in dealing with problems pertaining to communication insofar as it permits us to posit a stability of content among interlocutors. I also show how people can be classified as same‐sayers in different ways, viz. if they express the same (minimal) proposition/content or if they utter the same sentence. If A utters ‘I'm happy’ and B replies: ‘C said that too’, what B said can mean either that C said that A is happy—thus C and A expressed the same proposition—or that C utters the same words—they both utter ‘I'm happy’ and in so doing express different propositions, i.e. that A is happy and that C is happy respectively.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".