Bibliographic record
Abstract
This study delves into some aspects of suprasegmental features such as syllable structure, stress, and rhythm and compares them between NS and NNS. It is investigated in spectrograms and sound waveforms that 1. On the aspect of syllable structure in English, the onset and the coda in English syllable structure are characterized to have a maximum of 3 and 4 consonant clusters, respectively. In contrast, Korean allows only 1 single consonant in onset and coda position. This cross-linguistic difference gives rise to the insertion of the neutral vowel /ɨ/ to break up the consonant clusters in English words, in which the inserted vowel forms an independent wave chunk. 2. Refer to stress in English, it is universally recognized as every single English word or sentence consist of its own stress. On the contrary, Korean lacks stress placed at the level of the word. It follows that Korean EFL learners tend to put an approximately equal prominence on every syllable in a word and to exhibit a tendency to put a strong prominence particularly on the first syllable of a word with more than 2 syllables, which is dubbed an ‘initial prominence phenomenon’in this paper. 3. In relation to English rhythm, English is certainly a stress-timed rhythm, but Korean is a syllable-timed rhythm. The core differences between the stress-timed rhythm and the syllable-timed are on the form of ‘foot’, which is established when stressed and unstressed syllables occur in relatively regular alternating patterns in sentences, led to a phenomenon of that the number of feet depends on the timing of articulation within a whole sentence. “This paper finds significance in exhibiting suprasegmental features in visualization between NS and NNS, given that these features play a more important role than segmental ones. It can also serve a milestone for future researchers in the EFL phonetic filed.”
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.008 | 0.002 |
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".