WOMEN'S AUTONOMY AND UNINTENDED PREGNANCIES IN THE PHILIPPINES
Bibliographic record
Abstract
To date, very few studies have examined what contributes to unwanted and mistimed births in the Philippines. In a country where women have higher educational levels than their male counterparts, and their status is among the highest in Asia, it is expected that unwanted births will be low. The evidence, however, points to the contrary as 44% of births reported in the last five years were unintended. Using the 2003 Philippines National Demographic and Health Survey, this article focuses on married women who are currently pregnant and those who had given birth in the last five years. Multinomial logistic regression is employed to ascertain the risks of a recent birth/pregnancy being unwanted, mistimed or wanted. Regardless of women's status, having a final say in household and sexual matters with husbands lowers the risk of unwanted births but not mistimed births, calling into question the use of status variables such as education and wealth as indicators of women's autonomy. The success of implementing family planning programmes and policies in reducing unintended pregnancies underscores the importance of understanding how women are able (or unable) to make decisions surrounding their reproductive intentions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".