20. Did Grandmothers Enhance Reproductive Success in Historic Populations?
Bibliographic record
Abstract
Human reproductive success requires both producing children and making investments in the development of offspring. To a large extent these investments are made by the parents of the child, but researchers are now looking beyond the nuclear family to understand how extended kin, notably grandmothers, enhance reproductive success by making transfers to progeny of different kinds. The extent to which kin influence fertility and mortality outcomes may vary across different socio-economic and geographic contexts; as a result, an international comparative framework is used here to sharpen our understanding of the role of kin in reproduction. This chapter assesses the role of grandmothers in fertility outcomes in a comparative historical demographic study based on data from Scandinavia and North America. The individual-level data used are all longitudinal and multigenerational, allowing us to address the impact of maternal and paternal grandmothers on the fertility of their daughters and daughters-in-law. Attending to heterogeneous effects across space and time as well as within-family differences via the use of fixed effects models, we discover broader associations of the paternal grandmother with higher fertility across the four regions. We also find a general fertility advantage associated with the post-reproductive availability or recent death of the maternal grandmother in the four populations. Important variations across regions nevertheless exist in terms of the strength of the association and the importance of the grandmother’s proximity. Our interpretation is that grandmothers were generally associated with high-fertility outcomes, but that the mechanism for this association was co-determined by family configurations, resource allocation and the advent of fertility control.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".