Endogenous sources of variation in language acquisition
Bibliographic record
Abstract
A fundamental question in the study of human language acquisition centers around apportioning explanatory force between the experience of the learner and the core knowledge that allows learners to represent that experience. We provide a previously unidentified kind of data identifying children's contribution to language acquisition. We identify one aspect of grammar that varies unpredictably across a population of speakers of what is ostensibly a single language. We further demonstrate that the grammatical knowledge of parents and their children is independent. The combination of unpredictable variation and parent-child independence suggests that the relevant structural feature is supplied by each learner independent of experience with the language. This structural feature is abstract because it controls variation in more than one construction. The particular case we examine is the position of the verb in the clause structure of Korean. Because Korean is a head-final language, evidence for the syntactic position of the verb is both rare and indirect. We show that (i) Korean speakers exhibit substantial variability regarding this aspect of the grammar, (ii) this variability is attested between speakers but not within a speaker, (iii) this variability controls interpretation in two surface constructions, and (iv) it is independent in parents and children. According to our findings, when the exposure language is compatible with multiple grammars, learners acquire a single systematic grammar. Our observation that children and their parents vary independently suggests that the choice of grammar is driven in part by a process operating internal to individual learners.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".