Measurement of the coupling coordination relationship between the structures of secondary vocational school programs and industries in China
Bibliographic record
Abstract
Abstract Secondary vocational education faces constant challenges such as the absence of higher quality, industrial structure upgrading, and a mismatch between the supply of skills and the needs of a specific industry. Therefore, investigating the relationship between the structures of secondary vocational school (SVS) programmes and industries can help us understand their positive interactions and reduce skill mismatch, which is of great significance to the strategies of secondary vocational education and sustainable economic development. Studies on the interactive relationship between SVS programmes and industries and the use of quantitative methods are still lacking. To address this research gap, this paper uses the perspective of coupling and coordination to build a conceptual framework and a computation model to measure the relationship between the two. The method of grey relational analysis is utilized to explore the influencing factors between indicators of SVS programmes and the coupling coordination degree(CCD). Using data from Tianjin, China, the findings are as follows: The interaction between the structure of the SVS programme and industries shows that with the increase in the contribution of the SVS programme structure to the coupling system, the CCD between the two also increases. Compared with the primary and tertiary industries, the secondary industrial structure is more closely related to the SVS programme’s structure, the interaction between the two has a high degree of influence, and the correlation is relatively high between the indicators of secondary industry programmes in SVSs and the CCD. It is necessary to adjust the number of programmes, increase the number of students in the secondary-industry-related programmes and reduce the number of students in the programmes of the primary and tertiary industries to adapt to the needs of a dynamic industrial structure gradually. SVSs should improve programmes in primary and tertiary industries to enhance students’ skills, prepare them for a competitive labour market and strengthen students’ transition from school to work. This study found that the coupling coordination relationship between the two is affected by the following crucial factors: interaction between the two, the contribution of the programme’s structure, regional featured or key industries, and changes in admission policies by the local education authority.
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".