Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We extend to lexical-tone systems a model of second-language perception, the Perceptual Assimilation Model (PAM) (Best & Tyler, 2007), to examine whether/how native-language lexical-tone inventory composition influences perception of novel tone. Native listeners of Cantonese, Thai, and Mandarin perform a tone mapping-rating assimilation task. Listeners hear CV syllables bearing all tones of Cantonese, Thai, Mandarin, and Yoruba - languages with different tone inventories. They (1) map the tone they hear to the nearest native tone category, and (2) provide a goodness rating on a 5-point scale (5 = perfect). As predicted by the PAM, listeners assimilated non-native tones to the phonetically-closest native tone categories. Listeners attended primarily to pitch-contour, and secondarily to pitch-height, contrasts for the mappings. E.g., Mandarin listeners assimilated the Thai high “level” (phonetically mid-to-high-rising) tone to Mandarin rising tone 76% of the time, and to Mandarin high-level tone only 22% of the time. Also as predicted, all novel tones did not assimilate equally well to native categories; mappings received ratings between 2.9-4.1, averaging 3.5. The groups’ different patterns of results indicate that novel-tone perception is influenced by experience with the native-language tone inventory, and that listeners attend to gradient phonetic detail to assimilate novel tones to native-tone categories. This work is supported by NSF grant 0965227 to J.A.
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.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it