Learning mandarin tones at sentence level through training: A pilot study
Bibliographic record
Abstract
The effect of training for learning Mandarin tones on larger linguistic unit beyond the isolated tones was investigated. the participants were seven trainees and 5 control subjects. All were beginning level Mandarin learners enrolled in a second semester Chinese course in a US public university. the stimuli used for pre- and post test were three sentences. Two of the sentences were statements and one was a simple question. The mean length of the sentence was 11 characters. The training stimuli consisted 15 sentences and 48 phrases produced by four native Mandarin speakers. Individual training sessions were performed on a PC using Kay Elemetrics Sona Speech II Software. The training stimuli were blocked by speaker producing four training blocks of which consisted of 15 sentences and 48 phrases. Pearson correlation tests revealed that inter-sentence correlations ranged from 0.411 to 0.686.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".