The Tool Preference and Optimization Path of Teacher Education Policy: Content Analysis Based on 25 Policy Texts
Bibliographic record
Abstract
The achievement of teacher education policy goals is inseparable from the scientific selection and rational use of policy tools. This research is based on the perspective of policy tools, with the help of content analysis, according to sample selection, constructing a two-dimensional analysis framework, text analysis unit and data analysis logic for teacher education policy, and analyzes the policy tool preferences and laws in 25 teacher education policy texts. This study found that there are obvious differences and unbalanced characteristics in teacher education policy tools used in teacher education; command tools and capacity building tools are simple and diverse, and lower-level tools are insufficient; policy tools are biased towards long-term construction and ignore short-term planning; The lack of systemic transformative tools, and the lack of a scientific combination of policy tools. Propose balanced teacher education policy tools; optimize the combination of policy tools; increase incentive tools and system transformational tools; actively introduce voluntary tools to achieve the corresponding optimization path of teacher education governance with the participation of multiple subjects.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".