Bibliographic record
Abstract
A number of studies have used samples from Western societies to assess educational differences in the likelihood of unintended pregnancies and births, but the issue is vastly understudied in Korea. To fill the gap in the literature, this paper draws data from the two most recent rounds of the National Survey on Fertility, Family Health, and Welfare ( N = 20,669), and adopted a competing risks framework to estimate the hazards of an intended first birth and unintended first birth relative to no birth. Results from discrete-time event history analyses indicate that women’s educational attainment significantly reduces the odds of unintended first childbirth, even after accounting for various sociodemographic factors. Women with two- and four-year college degrees were 47.5% and 63.9% less likely to experience unintended childbearing compared with those without a high school degree. Women’s higher educational attainment is also negatively associated with the odds of intended childbearing relative to no birth. Hence, the present study suggests that there is a strong negative association between unintended childbearing and educational attainment among Korean women. These results are in accordance with Western patterns, and do not support an inverted correlation reported in Japan. Interaction between gender role ideology and educational attainment also indicated that women’s higher education tends to reduce the probability of experiencing unintended childbirth except when women have a very strong beliefs in gender equity. A better understanding on the determinants of unintended childbearing may be useful for identifying vulnerable segments of the population and preventing the intergenerational transmission of social disadvantages through fertility behaviors.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".