Predicting Scalar Implicature Interpretations From Lexical Knowledge
Bibliographic record
Abstract
PURPOSE: "some" to determine their abilities to generate "some, but not all" scalar implicatures or pragmatically enriched quantifier interpretations. We then determine the degree to which lexical development predicts implicature interpretations. METHOD: We fit regression models with lexical measures as predictor variables and implicature interpretations as the outcome variable. We then divide the child sample into implicature generators (50/61) and implicature nongenerators (11/61) and test the usefulness of the four lexical measures in a linear discriminant function analysis to separate children into these two categories. RESULTS: Results show significant correlations between each lexical measure and the outcome variable and, in a regression, that three of four lexical measures account for unique variance. Furthermore, the linear discriminant function analysis separates children into implicature nongenerators with 100% accuracy (11/11) and implicature generators with 88% accuracy (44/50). CONCLUSIONS: The Quantity Scale, or set of quantity-expressing determiners, proposed by Horn and Grice, develops as a function of the links among its quantifiers. We speculate that children's lexicons refract approximate number system representations in language- and morpheme-specific ways. These quantified noun phrases (NPs) are then merged into sentences interpreted pragmatically with conversationally computed implicatures, using higher order reasoning.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".