Bibliographic record
Abstract
The study examined the prevailing assumption of education’s role in labor market outcomes using samples from Korea's young adult population. KEEP, collected annually by KRIVET since 2004, includes an initial sample in 2004 of 12th graders from both general and vocational high schools; the sample size reflected a total of 2 000 students for each school type. In 2006, a similar sampling was taken with 11th graders from special-purposed high schools for study; the sample size reflected a total of 600 students. In this study, the respondents’ income-, social origin-, and education-related data were collected, and the multiple regression method was used to analyze the aforementioned data. The study examined the association between social origin and/or education and labor market outcomes, but given the prevalence of private tutoring in Korea, the study separated the examination of private tutoring recipients and compared their results to those of all general respondents. The findings revealed, against assumption, that the actual overall effect of education on income is weak, and there is no effect, especially, on private tutoring recipients. And if and when an association does exist, education appears to affect income negatively. On the other hand, social origin shows its statistical significance in its association with income across the groups; and among social origin components, the father’s educational level and employment type appear to be predictors.
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 | 0.000 | 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) | 0.002 | 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".