Problems and Solutions of English Teachers in Rural Middle Schools under the Background of Rural Revitalization
Bibliographic record
Abstract
The important task of rural revitalization is the revitalization of talents, which cannot be separated from the revitalization of rural education. The author believes that government departments should attach importance to the construction of teacher ethics[1] . Based on this, this paper mainly analyses the problems existing in rural English education and puts forward the corresponding countermeasures. The author believes that government departments should attach importance to the construction of teacher ethics of rural English teachers, rural English teachers should improve their teaching skills, and the teaching of English as a foreign language should be made more accessible to the public. Therefore, taking Longhui County of Hunan Province as an example, this paper mainly analyses the current situation of rural English education, analyses the existing problems of junior high school English teachers, and puts forward the countermeasures for the development of rural education in the future. In the author's opinion, government departments need to attach great importance to the construction of rural English teachers' moral team, English teachers to improve the professional education and teaching concepts for English junior high school teachers in rural areas, the Government to strengthen the education and training for rural junior high school English teachers, and to improve the salary and treatment of rural English teachers, so as to realize the modernization of rural revitalization in the context of rural revitalization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".