Bibliographic record
Abstract
In mainland China, more and more people have joined in the learning of playing piano. The conflict between many students and few teachers have become obvious, especially in music institutes of many universities. In our many years’ experiments, we have found out two ways to solve this problem: one is individual teaching, the other is group teaching, and we have made great success. Key words: mainland China, piano teaching, experiments Resume: Sur la Chine continentale , a la faculte de musique des universites normales , il y a de plus en plus d’etudiants qui se specialisent dans l’etude de piano . En ce cas , la methode traditionnelle d’enseignement ne peut plus satisfaire aux besoins sociaux . Au cours de longue pratique de l’enseignement , nous avons trouve la methode susceptible d’etre appliquee a l’enseignement du piano dans les universites normales , c’est-a-dire , la methode de combinaison entre le cours individuel et le cours collectif , et on a obtenu de grands succes . Mots-cles: la Chine continentale, l’enseignement du piano, experimentation 摘要：在中國大陸高等師範院校音樂系，鋼琴專業的學生數量持續增多，傳統的教學模式已滿足不了日益增長的社會需求。我們在長期的教學實踐中，探索出了一套適合高等師範鋼琴教學使用的方法，即個別授課與集體授課相結合的方法，取得了較好的教學效果。 關鍵詞：中國大陸；鋼琴教學；特色
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".