Bibliographic record
Abstract
Although unintended childbearing has declined in recent years (Finer and Zolna, 2016; Jones and Jerman, 2017), reducing unintended childbearing remains a public health goal in the U.S. due to its links to poorer outcomes for mothers, children, and families (Healthy People 2030). In this profile, we investigate trends in birth intendedness among women 15-44 between 1997 and 2018 using the 2002, 2006-10, 2011-15, and 2015-19 cycles of the National Survey of Family Growth 1 . Birth intendedness is based on a series of questions in which women were asked to characterize each birth as on time, mistimed (wanted but occurring earlier than desired), or unwanted (the respondent did not want any births at all or no additional births). When births were reported as mistimed, women were asked how much earlier than desired the birth occurred, and we categorize mistimed births into two groups: slightly mistimed (less than two years earlier than desired) or seriously mistimed (two or more years too early). This profile is an update of FP-17-08 and is the first in a three-part series on unintended fertility in the U.S.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".