How Nearby Homicides Affect Young Women's Pregnancy Desires: Evidence From a Quasi-Experiment
Bibliographic record
Abstract
Leveraging spatiotemporal variation in homicides that occurred during a 2.5-year weekly panel survey of 387 women ages 18-22 in Flint, Michigan, we investigate how young women's desires to become pregnant and to avoid pregnancy evolve in response to local homicides during the transition to adulthood. To address the endogeneity of exposure, we explore how the same woman's pregnancy desires (1) differed, on average, across weeks before and after the first homicide occurred within a quarter mile of her home; (2) evolved in the aftermath of this initial homicide exposure; and (3) changed in response to additional nearby homicides. One-fifth (22%) of women were exposed to a nearby homicide at least once during the study, and one-third of these women were exposed multiple times. Overall, the effects of nearby homicides were gradual: although average desires to become pregnant and to avoid pregnancy differed after initial exposure, these differences emerged approximately three to five months post-exposure. Repeated exposure to nearby homicides had nonlinear effects on how much women wanted to become pregnant and how much they wanted to avoid pregnancy. Together, our analyses provide a new explanation for why some young women-especially those who are socially disadvantaged-desire pregnancy at an early age.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".