Bibliographic record
Abstract
With the great, constant renovation and development of various knowledge and economy, talents of compound, high quality and high skills are in urgent need in society; a new educational reform runs through the whole foreign teaching courses, including audio-visual course, speaking, reading, writing and translating courses. With the aid of computers, films (DVD, Mp3 etc.) play an important role in foreign language labs in China. More and more students are interested in not only for oral but also for the process of acquiring languages. Now students are becoming stronger and stronger in their curiosity for knowledge and comprehension for acquiring languages. Therefore, the foreign teachers are confronted with the great challenges: 1) How to make audio-visual classes become effective learning process instead of pure entertainment in class; 2) How to make students become active participants in class. 3) How to practice rehearse the kinds of listening and speaking in the classroom? 4) How to help students build confidence in dealing with the language? 5) How to design classroom procedures on students’ listening and speaking abilities? So, the purpose of this paper is to introduce some useful and practical methods to build students’ confidence in learning English; and also to exploit the design of films’ class through multimedia.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".