Early Childbearing in Nicaragua: A Continuing Challenge In Brief
Bibliographic record
Abstract
•Among Nicaraguan women 20–24 years old, six in 10 had entered a union and almost half had had a child before their 20th birthday. •A quarter of all births in Nicaragua—35,000 per year—are to 15–19-year-olds. •Rural women, who have less education, on average, than their urban counterparts, are more likely than city dwellers to enter a union and become mothers during adolescence. •The proportion of 20–24-year-olds who had a child during adolescence is more than twice as high among the poorest as among those in the highest socioeconomic category. •Nearly half—45%—of births to adolescent women are unplanned, a level that varies little by women’s urban-rural residence and their educational achievement. •Among all sexually active women aged 15–19 (in union and not in union), 86 % do not want a child in the next two years, and 36 % have an unmet need for effective contraception. Unmet need for family planning is equally high in urban and rural areas. •The strong link between low educational attainment and early motherhood suggests that improving educational opportunities for girls is a promising way of reducing high levels of adolescent childbearing in Nicaragua. Early Childbearing in Nicaragua: A Continuing Challenge
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".