An Investigation of Metaphoric Cognition of First-Year College Students at Xinxiang Medical University, China
Bibliographic record
Abstract
The “College English Teaching Reform Project”, issued by the Chinese Ministry of Education aims to strengthen the practical English instruction and improve the English language proficiency of the college students (Ministry of Education, 2007). However, the problem of “naturalness” in handling English by the college students still exists due to imbalanced language forms and concepts between their native language (Chinese) and target language (English). Since metaphor was referred to as only a figure of speech and often compared with simile in high schools, many students do not realize that it also can be a powerful cognitive tool. In order to ascertain the students’ metaphor cognition competence in General English and Medical English, a metaphor cognition questionnaire was distributed in a class and the results obtained show that the respondents can readily recognize the existence of metaphors in General English and the teachers’ instruction, and they know the essential function of metaphors. The results further show that many respondents understand metaphors in terms of the context rather than the images. However, only about half of them can effectively use metaphors in their writing and speaking.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".