Model of English Teaching for Future Employees in China's Petroleum Production Industry
Bibliographic record
Abstract
Model of English teaching in non-native English speaking countries are very important in English Education, especially in the discipline of teaching English as a second language. Therefore English teaching design requires more specificity, especially for special purposes such as teaching English for industrial workers. Experience indicates that the “1+1 model”（one year for acquiring basic knowledge plus one year for site training and practices in oil fields）is an effective approach for teaching future oil field workers. The model follows four basic principles: establishing a target, cultivating standards, designing and following a process, and effective evaluation. Additionally, cooperative teaching and effective learning are encouraged in this model. Transitioning to the “1+1 model” requires not only a change in teaching methods or means, but also a philosophical shift in the concept of English instruction, that is, a move toward the realization of a “student-centered” approach, emphasizing self-study and the acquisition of practical skills. The method we used includes experimental method and interview. The result indicates that English teaching “1+1 model” can supply more qualified future employees for the petroleum production industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".