The Research on the Cultivation Mode of Teachers Teaching Chinese to Speakers of Other Languages
Bibliographic record
Abstract
The present major of Teaching Chinese to Speakers of Other Languages (TCSOL) take on a vigorous trend of development. However, there are also some some deficiencies in the cultivation of teachers. For example, the training of TCSOL didn’t match with the employment; students didn’t get enough teaching practice; the cultivation of overseas Chinese teachers didn’t have strong pertinence; Confucius institutes and overseas Chinese teaching institutes didn’t play enough part in the cultivation of teachers. In this regard, we need to innovate the training mode of TCSOL teachers from the following aspects: 1. Strengthen the running of schools, and transform the Chinese teaching institutes for international students into school-run enterprises. 2. Lay down complete rules for teaching practice and strictly enforce them. 3. Adopt the way of training based on the order to meet the quality and quantity requirement for overseas Chinese teachers. 4. Give full play to the role of Confucius institutes and overseas Chinese teaching institutes in teacher training. At last, we also need to further elaborate the teaching levels of the standard for TCSOL teachers , pay more attention on the research of TCSOL teachers’ employment and major development, and strengthen the cultivation of local Chinese 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".