A Corpus-Based Study of Semantic Collocations of the Verb “Feel” in English Public Speaking Setting: Chinese EFL V.S Native Speakers
Bibliographic record
Abstract
As great importance has been given to English in China, English public speaking is becoming an important part in students’ English learning. Meanwhile, many forms of English speaking contests have been flourishing in China over two decades. However, analysis of semantic prosodies in public speaking setting is relatively complex and subjective due to the difficulties in describing the evaluative meanings and the attitudes as intended by the speaker. This paper is a contrastive study of semantic prosody of the word feel which was concordanced from two sub corpora (CES_S and CES_C) from a self-built corpus (Corpus of English Speeches(CES)), aiming to explicitly reveal how EFL learners are different from native speaker when using a word with specific semantic prosody in English public speaking setting. The results show that there is no significant difference between Chinese students (EFL learners) and U.S and UK celebrities (Native English speakers) when they use the word feel with positive semantic preference in the context of delivering a speech, but EFL learners tend to express more positive emotions towards their counterparts than they do to themselves. In addition, the EFL learners have higher frequencies in using the word feel with the negative environment than the native English speaker do. This paper thus offers additional information on choosing the right semantic collocation and provide instructional guidance for English public speaking teaching and learning in China.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".