Modeling the consequences of an L1 grammar for L2 production: simulations, variation, and predictions
Bibliographic record
Abstract
Introduction This paper presents a constraint-based grammar of Mandarin low vowel + nasal coda (loVN) sequences first as acquired by L1 learners, and then as transferred to L2 English. Methods We simulate phonological learning in Harmonic Grammar using a gradual, error-driven GLA learner, drawing on evidence from L1 Mandarin speakers' perceptual data to support our initial state assumptions. We then compare our simulation results with L2 English production (both anecdotal and ultrasound data), as well as evidence from Mandarin loanword phonology. Results Our results align with multiple patterns in the previous empirical literature, including an asymmetry among surface repairs for VN sequences, and we show how these emerge from our assumptions about both the L1 Mandarin grammar and the grammar's evaluation method (i.e., weighted constraints). Discussion We discuss the extent to which these results derive from our somewhat novel analysis of place contrasts in L1 Mandarin, and the variability in loVN outputs that we encode directly into the L1 grammar, which are then transferred to the L2 context. Ultimately we discuss how this type of modeling can make falsifiable predictions about phonological development, in both L1 and L2 contexts.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".