Father’s Side or Mother’s Side? Lineage-Based Differences in the Impact of Grandparental Resources on Grandchildren’s Education in Japan
Bibliographic record
Abstract
This study examines the mechanisms through which grandparental resources and advantages increase the opportunities of grandchildren to access college, using longitudinal data from Japan. It also focuses on the heterogeneity in the transmission of grandparental advantages by lineage and gender. Although recent studies of social mobility have increasingly recognized the accumulation of inequality across multiple generations, relatively few studies have identified the underlying mechanisms or explored the roles of extended family structures in shaping multigenerational mobility. To advance this line of inquiry, we investigate how grandparental resources and contact are associated with grandchildren’s college enrollment in Japan—a context characterized by strong intergenerational ties and mutual support. We draw on two key mechanisms to explain multigenerational social mobility: the contact-based and noncontact-based mechanisms. In addition, we examine variation by grandparents’ lineage and gender. Our findings show that contact with paternal grandparents and grandfathers on both sides facilitates the transmission of their advantages to grandchildren, whereas no such pattern is observed for maternal grandmothers. These results underscore the importance of contact with grandfathers and patrilineal lineages in shaping multigenerational mobility in Japan. This contrasts with studies based on U.S. data, which emphasize the role of grandmothers. To deepen our understanding of multigenerational mobility, future research should shed light on how grandparental contact and resources operate across diverse family and societal settings.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".