The Tool Preference and Optimization Path of Teacher Education Policy: Content Analysis Based on 25 Policy Texts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it