A Study of the Correlation Between Junior High School Students' Cultural Awareness of English and English Scores
Bibliographic record
Abstract
Cultivating language cultural awareness helps enhance students' national identity and national sentiment, improve their sense of language cultural identity and self-confidence, and facilitate their growth into socially responsible and civilized individuals. Mainland Chinese students use the British language "English" as a second language in their language learning process. Therefore, developing students' cultural awareness of English becomes an essential part of teaching. At present, most studies by Chinese researchers on English cultural awareness have focused on its current state and the way it is cultivated, and there are few studies on the correlation between English cultural awareness and English scores. Thus, this study will attempt to explore the correlation between junior high school students' cultural awareness of English and their English scores. The experiment showed that there was a significant positive correlation between English cultural awareness and English scores (r=0.742, p<0.01), that is, the more cultural awareness you are of English, the more your English scores will improve significantly. The findings of this study help researchers of English language teaching and teachers to further understand the importance of cultural awareness in English and the correlation between English cultural awareness and English scores.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".