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 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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".