Allophonic alternations influence non-native perception of stress.
Bibliographic record
Abstract
We examined the identification of stressed syllables by adult L2 Spanish learners to see if it is influenced by an allophonic alternation driven by word position and stress. We utilized the Spanish voiced stop-approximant alternation, where stops occur in word onsets and stressed-syllable onsets. If L2 learners track the distribution of this alternation, they should link stops to stressed syllables in word onset position and approximants to unstressed, word medial position. Low- and Intermediate-level L1 English/L2 Spanish learners, Native Spanish and monolingual English speakers listened to a series of CVCV nonce words and determined which syllable they perceived as stressed. In Experiment 1, we crossed onset allophone and vowel stress. In Experiment 2, we alternated the onset allophone and held the vowel steady. Our results show that less experienced groups were more likely to perceive stressed vowels and approximant onset syllables as stressed. This suggests that learning the interplay between allophonic distributions and their conditioning factors is possible with experience. L2 learners track distributions in the input and this, in turn, influences their perception of other properties in the language, in this case, syllable stress. Native language distributions and target language proficiency play a role in this process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".