The Professional Development of English Teachers in Training Institutions from the Perspective of “Double Reduction Policy”—A Case Study on S Institution
Bibliographic record
Abstract
Recently, “double reduction policy” has been issued throughout China, which encourages English teachers in training institutions to accommodate their professional development. More researches are demanded to be done so as to cope with the challenge. This research mainly focuses on the professional development of English teachers in training institutions from the perspective of the “double reduction policy” taking S institution in Chengdu City as an example and 30 teachers in S institution as research participants in order to bolster the professional development of English teachers in training institutions. And this investigation can be fulfilled through qualitative and quantitative research methods including the literature analysis, questionnaire investigation and some related interviews so as to solve the following research questions: 1) What is the status quo on the professional development of English teachers in training institutions in the context of double reduction policy? 2) What factors may affect the professional development of English teachers in training institutions in the context of double reduction policy? 3) What valid and feasible strategies can be induced to enhance the professional development of English teachers in training institutions from the perspective of double reduction policy? Based on the results of research questions, this research project will give an advisable direction for those English teachers in training institutions to adjust themselves to the double reduction policy.
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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 | 0.003 | 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 | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".