Bibliographic record
Abstract
Previous studies have shown that a word’s phonological similarity to other words (i.e., phonological neighborhood) can influence its recognition. However, most research concerning lexical representations has been observed for neighbors based on segmental overlap, and little is known about such effects with suprasegmentals such as Mandarin tones. In the present study, two experiments were conducted with forty L2 listeners and 40 native speakers to examine how tone neighborhood density influences Mandarin spoken word recognition. In Experiment 1, speed and accuracy from both groups’ performance in an auditory lexical task were influenced by tone neighborhood density (i.e., fewer words were recognized from dense tone neighborhoods than from sparse tone neighborhoods). However, L2 listeners’ performance was inferior to native listeners’. In Experiment 2, form priming patterns showed that reliable facilitation was observed only when the prime and the target were identical, while monosyllabic Mandarin words differing only in tone failed to speed the response to the target. In addition, only L2 listeners showed an increase in RTs to respond to the target when it was preceded by tone overlap primes. The results of these experiments demonstrate that tone neighborhood is an important factor in L2 Mandarin spoken word recognition.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".