Do Data from Large Personal Networks Support Cultural Evolutionary Ideas about Kin and Fertility?
Bibliographic record
Abstract
The fertility decline associated with economic development has been attributed to a host of interrelated causes including the rising costs of children with industrialization, and shifts in family structure. One hypothesis is that kin may impart more pro-natal information within their networks than non-kin, and that this effect may be exacerbated in networks with high kin-density where greater social conformity would be expected. In this study, we tested these ideas using large personal networks (25 associates of the respondent) collected from a sample of Dutch women (N = 706). Kin (parents) were perceived to exert slightly more social pressure to have children than non-kin, although dense networks were not associated with greater pressure. In contrast, women reported talking to friends about having children to a greater extent than kin, although greater kin-density in the network increased the likelihood of women reporting that they could talk to kin about having children. Both consanguineal and affinal kin could be asked to help with child-care to a greater extent than friends and other non-kin. Overall, there was mixed evidence that kin were more likely to offer pro-natal information than non-kin, and better evidence to suggest that kin were considered to be a better source of child-care support.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".