Remarks on the Experimental Turn in the Study of Scalar Implicature, Part I
Bibliographic record
Abstract
Abstract (for Part I and Part II) There has been a recent ‘experimental turn’ in the study of scalar implicature, yielding important results concerning online processing and acquisition. This paper highlights some of these results and places them in the current theoretical context. We argue that there is sometimes a mismatch between theoretical and experimental studies, and we point out how some of these mismatches can be resolved. We furthermore highlight ways in which the current theoretical and experimental landscape is richer than is often assumed, and in light of this discussion, we offer some suggestions for what seem to us promising directions for the experimental turn to explore. The article is divided in two parts. Part I first presents the two dominant families of accounts of scalar implicature, the domain‐general Gricean account and the domain‐specific grammatical account. We try to separate the various components of these theories and connect them to relevant psycholinguistic predictions. Part II examines and reinterprets several prominent experimental results in light of the theoretical presentation proposed in the first part.
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 | 0.040 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.006 | 0.020 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 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".