Examining the Role of Phoneme Frequency in First Language Perceptual Attrition
Bibliographic record
Abstract
In this paper, we follow up on previous findings concerning first language (L1) perceptual attrition to examine the role of phoneme frequency in influencing variation across L1 contrasts. We hypothesized that maintenance of L1 Korean contrasts (i.e., resistance to attrition) in L1 Korean-L2 English bilinguals would be correlated with frequency, such that better-maintained contrasts would also be more frequent in the L1. To explore this hypothesis, we collected frequency data on three Korean contrasts (/n/-/l/, /t/-/t*/, /s/-/s*/) and compared these data to perceptual attrition data from a speeded sequence recall task testing the perception and phonological encoding of the target contrasts. Results only partially supported the hypothesis. On the one hand, /n/-/l/, the best-maintained contrast, was the most frequent contrast overall. On the other hand, /n/-/l/ also evinced the greatest frequency asymmetry between the two members of the contrast (meaning that it was the least important to perceive accurately); furthermore, /s/-/s*/, which was less well maintained than /t/-/t*/, was actually more frequent than /t/-/t*/. These results suggest that disparities in perceptual attrition across contrasts cannot be attributed entirely to frequency differences. We discuss the implications of the findings for future research examining frequency effects in L1 perceptual change.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".