Vol. 2: The Excellence of Technical Vocational Education and Training (TVET) Institutions in Korea: Case Study on Busan National Mechanical Technical High School
Bibliographic record
Abstract
This study is a series of the empirical study, the Excellence of Technical Vocational Education and Training (TVET) Institutions, which has investigated the association between four premise factors (competent teachers, relevant curricula, effective leadership, and school-industry linkages) and school performance. The purpose of the study is to provide recommendations to individual institutions who seek to develop strategies to improve their internal and external efficiencies as well as provide policy makers with empirical evidence to help develop new TVET policies that increase schools’ responsiveness to industry demands and reduce skills gap. The study assessed (1) whether or not the select school Busan National Mechanical Technical High School (BMT) possesses four premise factors; (2) how these factors contribute to the enhancement of school outcomes, and (3) which factor has the most influence in differing contexts (e.g. TVET policy, labor market conditions, social demands) and times. The selection criterion was the school’s high graduate employment rate. The study gathered data via multiple resources, including school publications, survey, and interviews. As for the survey, 555 out of 600 students and 107 out of 113 teachers responded. The interview was conducted with 10 students, 10 specialty teachers, the principal, and one vice principal. The interview style was an in-person, one-on-one with structured, open-ended questions, where each interviewee was sequestered separately in a closed room for 60 minutes. After coding the raw data, certain themes emerged. The findings suggest that BMT possesses all the stated premise factors, and the factors directly or indirectly influence the graduate employment rate via the enhancement of employability. Additionally, the most influential factor can be altered based upon various contexts and times.
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 | 0.001 | 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 | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".