Problems and Solutions in Bi-Foreign-Language Talents Cultivation in China’s Universities
Bibliographic record
Abstract
To cater the needs of social development, some universities in China set the program of bi-foreign-language study in recent years. As a new program, some problems still exist, which include learning hours, teaching methods, learning difficulties and teacher training. In this article, we have summarized the problems in the talent cultivation mode from four perspectives. First, too many learning hours in class and great stress from learning are a prevailing problem among the students. When setting the learning hours of the curriculum, universities should try to find a balanced point between teaching time and students’ learning stress. Second, the problem in teaching method is that teachers rarely make contrastive analysis of the languages. It is suggested to make comparison or contrast between L2 and L3, so as to improve the efficiency of classroom teaching. Third, the difficulty encountered by the students is lack of time for autonomous study after class to ensure the learning of both languages. Teachers must help them improve their efficiency by utilizing their language aptitudes and apply appropriately their language learning strategies to L3 learning. Lastly, lack of trilingual teachers has become an obstacle hindering the development of the program, so there is an urgent need for the universities to provide training for teachers.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".