Leaving Obligations Behind: Epistemic Incrementation in Preschool English
Bibliographic record
Abstract
Does language development drive language change? A common account of language change attributes the regularity of certain patterns to children’s learning biases. The present study examines these predictions for change-in-progress in the use of must in Toronto English. Historically, modal verbs like must start with root (deontic) meanings, eventually developing epistemic (probability) meanings in addition. Epistemic uses increase over successive generations, phasing out root uses (incrementation). The modal becomes unambiguously epistemic and eventually disappears from the language. Such cyclic changes are predictable and common across languages. To explore whether children contribute to incrementation and loss, we tested intuitions about must in preschoolers (n = 141) and adults (n = 29). In a picture-preference task (deontic vs. epistemic), children selected epistemic interpretations of ambiguous sentences (e.g., Michelle must swim) at higher rates than adults. Two context-based preference tasks tested children’s overall sensitivity to the presence of modals. We found sensitivity in deontic contexts. In epistemic contexts, where must is optional and functions like an evidential marker, we found little discrimination, and general avoidance of the modal. These results (epistemic overgeneration, must-avoidance) correspond to predictions of the incrementation hypothesis, suggesting children likely play an active role in language change, beyond well-known overregularization processes.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".