Bibliographic record
Abstract
This paper tries to probe into the current situation of MBA English teaching in our country and that of ZNUEL. Based on the analysis of MBA English course design, teaching approach as well as the structure of English teachers and MBA candidates, the author finds out some problems existing in MBA English teaching field. Key words: MBA English teaching; Problems; Current; situation Resume: Cet article tente d’explorer la situation actuelle de l'enseignement de MBA en anglais dans notre pays et celle de ZNUEL. Base sur l'analyse de la conception des cours de MBA en anglais, les methodes d'enseignement ainsi que la structure des enseignants d'anglais et des candidats MBA, l'auteur decouvre des problemes existants dans le domaine de l'enseignement de MBA en anglais. Mots-cles: L’enseignement de MBA; en anglais; Problemes; Situation actuelle 摘 要：本文的目的在於探討MBA 商務英語課程的課程設置和教材、授課方式、教學大綱、師資隊伍、學員結構等方面的現狀，在此基礎上對MBA 英語教學遇到的問題進行分析和說明。 關鍵詞：課程設置；授課方式；教學大綱；師資隊伍；學員結構
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".