Bibliographic record
Abstract
Language is a part of culture, and plays a very important role in the development of the culture. Some sociologists consider it as the keystone of culture. They believe, without language, culture would not be available. At the same time, language is influenced and shaped by culture, it reflects culture. Therefore, culture plays a very important part in language teaching, which is widely acknowledged by English teaching circle. This thesis depicts the relationship between culture and language. As a result, the gap of cultural differences is one of the most important barriers in English teaching and study. Among the students, lacking of cultural background knowledge can, to a great extent, hold up the improvement of English teaching and become a noticeable problem. At present, the objective of English teaching has broken free from the traditional listening, speaking, reading and writing, and the demand for cultural background knowledge in language learning has been gradually concerned. Presentation of history of the country which has the target language, cultural background knowledge and customs is the proposed solution to the problem. This paper mainly discusses how to present cultural background knowledge and expose learners to it in the need of English teaching at Chinese schools so as to solve the problems caused by cultural differences, help learners grasp the crux of the language and develop their comprehensive English ability.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".