Bibliographic record
Abstract
提要 本文以笔者所任教的加拿大大学生为研究对象，首先对超过语言习得“关键期”的学习者能够获得母语一样的语音进行验证，然后探讨成年学习者学习汉语语音的成功学习策略。本文的研究证实，成年人若使用适当的学习策略，就能够成功获得标准的现代汉语语音。成功的学习者都有较强的学习动机和主动学习的意识，而学习策略则是学生主动学习的一个工具。同样的课堂教学及教材教法，不同学习者理解、记忆直至获得目的语语音所采用的学习计划安排，及使用的途径方法等有所不同，甚至有明显的差别。在不同的场合情景下选择使用相应的学习策略，能够调动学生的组织能力和自我监控意识，让学生使用已有的知识解决学习中的新问题，通过与同学老师或汉语为母语者的互动，提高语音水平，进而帮助学生完成学习任务实现学习目标。
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".