Achievements and Difficulties - Review Ten Years of Media Literacy Education in China
Bibliographic record
Abstract
Relevant research and practices of China's media literacy education have a history of ten years. It achieved rapid development through the efforts from all walks of life. Combined with the experience of developed countries in the world, this article elaborates on the achievement of media literacy education as well as analyzes the difficulties faced with China’s media literacy education from theoretical research, mass base, government support, teacher training and other aspects. It also tries to offer some suggestion and opinions. Key words: Media literacy education, China, achievements, difficulties Resume: La recherche et les pratiques en question de l’enseignement secondaire en Chine ont une histoire de dix ans. Elles ont accompli le developpement rapide par les efforts de toutes les demarches de la vie. Combinee avec les experiences des pays developpes dans le monde, cette memoire s’etend sur les reussites de l’enseignement secondaire ainsi que analyse les difficultes faites face a l’enseignement secondaire en Chine de la recherche theoretique, la masse de base, les soutiens gouvernementaux, la formation pour les professeurs et d’autres aspects. Elle essaie aussi de proposer des conseils et des opinions. Mots-Cles: enseignement secondaire, Chine, reussites, difficultes
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".