Racial/ethnic variation in partnered fathers’ time with children: incorporating diverse fathering logics
Bibliographic record
Abstract
Through a socioeconomic/demographic lens and with a minority logic perspective, this study examines variation in partnered fathers’ time spent with residential young, elementary-school-age, and adolescent children across Latino, Asian, Black, and White fathers, using data from the 2003 to 2019 American Time Use Survey (N = 26,556). Some observed racial/ethnic differences in how fathers spend time with their children are associated with differences in socioeconomic status (SES), immigration status, and breadwinning responsibilities, but some remain significant and other differences emerge, after controlling for these factors. Compared with fathers in the three other groups, Latino fathers spend more copresent time with young children in daily routines, consistent with Latinx communities’ family-focused model. Asian fathers spend more time with adolescents in educational activities, reflecting Asian communities’ emphasis on educational achievements. Black fathers spend less time with children in terms of the total amount but more time in religious activities with young and elementary-school-age children, reflecting Black communities’ communal parenting. White fathers spend more time playing with young children, consistent with the mainstream ideal. Findings suggest the importance of considering each community’s unique sociohistorical location and childrearing logic in understanding racial/ethnic variation in father involvement to best capture minority parents’ daily experiences.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".