The enlightenment and the impetus of the new approach of the Common European Framework of Reference for Language on the Chinese language teaching
Bibliographic record
Abstract
The Common European Framework of Reference for Language (CEFRL) is developed through a process of several decades of scientific research and wide consultation, under the authority of the Council of Europe. It is the result of extensive research and ongoing work on communicative objectives, as exemplified by the popular Threshold level concept. This document provides a practical tool for setting clear standards to be attained at successive stages of learning and for evaluating outcomes in an internationally comparable manner. A European Union Council Resolution (November 2001) recommended the use of this Council of Europe instrument in setting up systems of validation of language competences. The influence of the CEFR in the fields of course design, textbook writing, language teaching, testing and training is wider and wider among the 47 member countries of the Council of Europe and the observer countries, as USA, Canada and Japan. The Chinese language teaching in France is growing fast, and some good results have been obtained in the field of compatibility between Chinese language and CEFRL. Facing the new epoch of education of promoting both language policy and language standards on the basis of the communicative approach, international Chinese language teaching cannot stay on the edge of the road and have to take up these new challenges.
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 | 0.061 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 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".