Pathways for Constructing China’s Highly “Dual-Qualified” Teaching Force in the New Era: A Textual Analysis of Nine Chinese Vocational Education Policies (2019–2025)
Bibliographic record
Abstract
Abstract Against the backdrop of industrial transformation and the digital transition of vocational education in the new era, building a highly “dual-qualified” teaching force has become a pivotal issue in advancing the high-quality development of China’s vocational education. Based on nine nationally issued vocational education policy documents from 2019 to 2025, this study employed textual analysis and qualitative research methods to systematically examine the pathways for constructing a highly “dual-qualified” teaching force, utilizing a three-tier coding framework to elucidate the policy priorities and focal points in China’s efforts. The findings reveal that the current development of “dual-qualified” teachers in China primarily advances along four dimensions: (1) standardizing the entry mechanism for “dual-qualified” teachers to ensure their possession of “dual qualifications”; (2) fostering “dual competencies” through industry-education collaborative teacher training; (3) establishing a post-employment professional development support system to facilitate continuous improvement in teachers’ expertise; and (4) refining incentive and evaluation mechanisms to motivate vocational education teachers to attain and enhance their “dual-qualified” capabilities. The study summarizes four key policy pathways for China’s high-level “dual-qualified” teacher development, shares successful experiences in this endeavor, and contributes to broadening global perspectives on vocational education teacher development research.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| 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".