Modeling age of exposure in L2 learning of vowel categories
Bibliographic record
Abstract
Modeling age of exposure in L2 learning of vowel categories Meghan Clayards McGill University Joseph Toscano University of Iowa Abstract: Age of exposure is known to be an important indicator of second language proficiency. Native-like phonological proficiency is attained only by learners exposed at the earliest ages. This paper examines one account of age-of-exposure effects. Two computational models (a mixture of Gaussians and a neural network) were trained without supervision on F1 and F2 tokens based on production data from two different vowel systems (Quichua and Spanish; Guion, 2003). Both models learn the individual phonological systems when trained on monolingual distributions. When exposed to bilingual data, both models also achieve varying degrees of success depending on when the second language (Spanish) is introduced in training, paralleling data from bilingual speakers with different ages of acquisition (Guion, 2003). This demonstrates that learners may be restricted in learning a second language not because of a biological critical period, but by the commitments that the system has already made to the first language.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".