Meta-Analysis of the Development of Teacher Education in University in the Educational Research of China
Bibliographic record
Abstract
This research is based on 67 articles related with the meta-analysis of the teacher education in the university, which was published on CNKI from 2001 to 2014. The study found that: Articles about this field were mainly published in 2011, presenting a falling and curving trend as a whole. The main 6 authors of these articles include Zhu Xudong, Zhou Jun, Shen Qibiao, He Xi, Zhang Guoqiang and Zhao Binghui while the main Research institutes involve Beijing Normal University, Fujian Normal University and Southwest University. However, little inter-academic cooperation has been conducted among authors and universities. These articles are mainly in the fields of comparative education and higher education, which focus on the formation path, the institutional environment, teacher education models and evaluation in universities.
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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 | 0.053 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.030 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".