Remarks on the Experimental Turn in the Study of Scalar Implicature, Part I
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it