Analysis of Chinese EFL Postgraduates' Experiences with Public Speaking Anxiety toward International Conference Presentation
Bibliographic record
Abstract
Anxiety has a significant effect on oral communication, particularly when it occurs in the form of a public address. The quality of a public speaker's oral presentation may highly be determined by a variety of emotive elements. However, this has received much too little attention in the realm of academic conference presentations, despite the fact that this process may be incredibly nerve-wracking for both novice and experienced postgraduate students. In the current study, 137 Chinese EFL postgraduate students consented to complete a revamped version of the Personal Report of Public Speaking Anxiety (PRPSA). Chinese EFL postgraduates reported a high level of public speaking anxiety during their international conference presentations, as measured by three categorical variables: public speaking apprehension, self-behavior management, and fear of negative evaluation. During the international conference presentations, ten questionnaire items were recognised to be the most anxiety-provoking conditions in terms of public speaking anxiety. In addition, differences in gender and graduate study specialization were not significantly associated with Chinese EFL postgraduates' experiences with public speaking anxiety. Nevertheless, it was discovered that Chinese EFL graduate students reported statistically significant levels of public speaking anxiety, and pedagogical suggestions were offered.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".