Shorthand, Syntactic Ellipsis, and the Pragmatic Determinants of What Is Said
Bibliographic record
Abstract
Abstract: Our first aim in this paper is to respond to four novel objections in Jason Stanley's ‘Context and Logical Form’. Taken together, those objections attempt to debunk our prior claims that one can perform a genuine speech act by using a sub‐sentential expression—where by ‘sub‐sentential expression’ we mean an ordinary word or phrase, not embedded in any larger syntactic structure. Our second aim is to make it plausible that, pace Stanley, there really are pragmatic determinants of the literal truth‐conditional content of speech acts. We hope to achieve this second aim precisely by defending the genuineness of sub‐sentential speech acts. Given our two aims, it is necessary to highlight briefly their connection—which we do in the first part of the Introduction. Following that, we introduce Stanley's novel objections. This is the role of the second part of the Introduction. We offer our rebuttals in Section 2 (against ‘shorthand’) and Section 3 (against syntactic ellipsis, among other things).
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".