The Determinants of the Transition in South Korea from Vocational and General High School to Higher Education, Including a Gender Comparison
Bibliographic record
Abstract
This study examines the factors determining whether vocational and general high school students in South Korea subsequently graduate from university and, if so, whether from 2-year or 4-year courses, for the first time using a gender lens. High-quality official data from the Korean Education and Employment Panel (KEEP) is used in a multinomial logit model. The results show that coming from a vocational high school (compared to a general high school) is negatively correlated with going to university, especially to 4-year university. Among general high school graduates, the most important determinant of attending a 4-year rather than a 2-year university is the teacher assessment of the student’s performance; father’s education and income have no effect for either males or females. The results also show that vocational high school graduates’ university choice is determined by a combination of individual characteristics, including being male, and by having been at a vocational high school, whereas the choice between 2-year and 4-year university depends negatively on father’s education for males but not for females and on father’s income and the number of siblings for both genders. The income and sibling findings suggest that a possible policy implication might be to provide financial support to vocational high school graduates to enable them to attend higher education and to offset the negative effect of low paternal income.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".