A Research Note on Time With Children in Different- and Same-Sex Two-Parent Families
Bibliographic record
Abstract
Public debate on same-sex marriage often focuses on the disadvantages that children raised by same-sex couples may face. On one hand, little evidence suggests any difference in the outcomes of children raised by same-sex parents and different-sex parents. On the other hand, most studies are limited by problems of sample selection and size, and few directly measure the parenting practices thought to influence child development. This research note demonstrates how the 2003-2013 American Time Use Survey (n=44,188) may help to address these limitations. Two-tier Cragg's Tobit alternative models estimated the amount of time that parents in different-sex and same-sex couples engaged in child-focused time. Women in same-sex couples were more likely than either women or men in different-sex couples to spend such time with children. Overall, women (regardless of the gender of their partners) and men coupled with other men spent significantly more time with children than men coupled with women, conditional on spending any child-focused time. These results support prior research that different-sex couples do not invest in children at appreciably different levels than same-sex couples. We highlight the potential for existing nationally representative data sets to provide preliminary insights into the developmental experiences of children in nontraditional families.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".