Talker-Specific Generalization of Pragmatic Inferences based on Under- and Over-Informative Prenominal Adjective Use
Bibliographic record
Abstract
According to Grice's (1975) Maxim of Quantity, rational talkers formulate their utterances to be as economical as possible while conveying all necessary information. Naturally produced referential expressions, however, often contain more or less information than what is predicted to be optimal given a rational speaker model. How do listeners cope with these variations in the linguistic input? We argue that listeners navigate the variability in referential resolution by calibrating their expectations for the amount of linguistic signal to be expended for a certain meaning and by doing so in a context- or a talker-specific manner. Focusing on talker-specificity, we present four experiments. We first establish that speakers will generalize information from a single pair of adjectives to unseen adjectives in a speaker-specific manner (Experiment 1). Initially focusing on exposure to underspecified utterances, Experiment 2 examines: (a) the dimension of generalization; (b) effects of the strength of the evidence (implicit or explicit); and (c) individual differences in dimensions of generalization. Experiments 3 and 4 ask parallel questions for exposure to over-specified utterances, where we predict more conservative generalization because, in spontaneous utterances, talkers are more likely to over-modify than under-modify.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".