A Study on the Typological Reform of China’s Vocational Education from a Comparative Education Perspective
Bibliographic record
Abstract
Abstract Taking comparative education perspectives and typological education theory as the analytical framework, this paper examines the evolutionary logic of China’s vocational education in terms of institutional construction, reform pathways, and cultural tension, and reveals its structural dilemma between institutional legitimacy and social recognition. By comparing Germany’s industry co-governance model, France’s qualification hierarchy and outcome transformation mechanisms, and Japan’s enterprise-oriented and job-matching approach, the study analyzes how these three countries address the tensions inherent in vocational education as well as the limitations of their respective approaches. The research argues that China’s typological reform should, in governance, strengthen multi-stakeholder co-governance; in reform pathways, improve outcome transfer and vertical articulation; and in institutional culture, enhance legitimacy and social recognition, thereby realizing the transition from policy advocacy to institutional stabilization. Through this comparative study, the paper seeks to uncover shared structural logics and propose locally generated pathways across the three dimensions of governance, pathways, and social identity, offering theoretical support and international reference for the typological reform of vocational education with Chinese characteristics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".