Exploring the dynamic metaphor patterns in describing English public speaking anxiety among Chinese learners
Bibliographic record
Abstract
Abstract This study aims to explore the dynamic metaphor patterns in describing English public speaking (EPS) anxiety among Chinese learners. Metaphor is frequently used to describe complex emotional states, mental processes, and difficult experiences (Kövecses, Zoltán. 2003. Metaphor and emotion: Language, culture, and body in human feeling . Cambridge: Cambridge University Press). This investigation is based on the discourse dynamics approach (Cameron, Lynne & Robert Maslen (eds.). 2010. Metaphor analysis: Research practice in applied linguistics, social sciences and the humanities . Toronto: University of Toronto Press). Fifteen Chinese learners were interviewed to present their EPS anxiety experiences in three speech types. A blended approach (combining naturalistic and elicited metaphors) was employed in the interviews. A total of 2006 metaphor vehicle terms were identified from the transcripts of interviews. The fitted log-linear model did not retain the highest level of the three-way interaction between VEHICLE GROUPING, TOPIC TERM and SPEECH TYPE. However, two possible bivariate associations (i.e., VEHICLE GROUPING * TOPIC TERM, and SPEECH TYPE * TOPIC TERM) were retained and discussed as metaphor patterns. In terms of the topics, metaphors of ANXIETY and OTHER (classroom environment, task demands, teacher feedback, peer pressure, emotional states of other people) were used more to describe EPS anxiety in the first informative speech but less in the third persuasive speech.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".