Bibliographic record
Abstract
The aim of the present empirical study is two-fold. The first aim is to investigate why Thai university students perceive a certain tone better than others or why a certain tone is more difficult to perceive than others. The second aim is to examine to what extent Thai university students can perceive four Chinese Mandarin tones. 14 volunteer university students (2 males; 12 females) participated in the study. Research tools were structured interview and the perception test. The findings from the interview reveal that 9 out of 14 (64%) students claimed that tone 4 was the easiest tone either to perceive or produce. In contrast, 10 out of 14 (71%) stated that tone 3 was the most difficult one to perceive. The qualitative data findings from the interview were greatly consistent with the quantitative data ones from the perception test. That is, Thai speakers performed well in tone 4 (mean scores 24.92 or 99.68%) and tone 1 (24.35 or 97.40%). On the other end of the scale, they had some difficulty identifying tone 2 (21.42 or 85.68%) and tone 3 (19.50 or 78%). It can be concluded that firstly, the hierarchy of tone accessibility from the least difficult to the most difficult one was tone 4 > tone 1 > tone 2 > tone 3. Secondly, students’ native language (Thai) or L1 plays a crucial role to their tonal acquisition when Thai speakers deal with foreign lexical tones. For one important reason, tones 1 and 4 in Chinese are very similar to the mid tone and the falling tone in Thai, respectively.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".