Advanced School Progression Relative to Age and Early Family Formation in Mexico
Bibliographic record
Abstract
Research has documented a negative association between women's educational attainment and early sexual intercourse, union formation, and pregnancy. However, the implications that school progression relative to age may have for the timing and order of such transitions are poorly understood. In this article, I argue that educational attainment has different implications depending on a student's progression through school grades relative to her age. Using month of birth and age-at-school-entry policies to estimate the effect of advanced school progression by age, I show that it accelerates the occurrence of family formation and sexual onset among teenage women in Mexico. Focusing on girls aged 15-17 interviewed by a national survey, I find that those who progress through school ahead of their birth cohort have a higher probability of having had sex, been pregnant, and cohabited by the time of interview. I argue that this pattern of behaviors is explained by experiences that lead them to accelerate their transition to adulthood compared with same-age students with fewer completed school grades, such as exposure to relatively older peers in school and completing academic milestones earlier in life. Among girls who got pregnant, those with an advanced school progression by age are more likely to engage in drug use, alcohol consumption, and smoking before conception; more likely to have pregnancy-related health complications; and less likely to attend prenatal care visits. Thus, an advanced school progression by age has substantial implications for the health and well-being of young women, with potential intergenerational consequences.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".