Bibliographic record
Abstract
Our exposition is framed around two questions: What interpretive effects can linguistic utterances have? What causes those effects? Lepore and Stone make an empirical case that some effects are contributions to the public record of a conversation determined by linguistic conventions—following Lewis—while non-contributions (our term) produced by imagination offer no determinate content—following Davidson. They thereby replace the old semantics–pragmatics divide by eliminating conversational implicature altogether. We critique Lepore and Stone’s position on empirical grounds, presenting cases in which contributions are made non-conventionally. We also critique their view methodologically, presenting a dilemma by which they either cannot handle many cases using their framework or they do so in an ad hoc fashion. We conclude by suggesting Relevance Theory as an alternative that follows Lepore and Stone’s purported methodology and handles many of their empirical cases.
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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.010 | 0.042 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.017 | 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".