A model explaining the matrilateral bias in alloparental investment
Why this work is in the frame
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Bibliographic record
Abstract
Maternal grandmothers invest more in childcare than paternal grandmothers. This bias is large where the expression of preferences is unconstrained by residential and lineage norms, and is detectable even where marriage removes women from their natal families. We maintain that the standard evolutionary explanation, paternity uncertainty, is incomplete, and present an expanded model incorporating effects of alloparents on the mother as well as on her children. Alloparenting lightens a mother's load and increases her residual nepotistic value: her expected fitness from later investments in personal reproduction and in her natal relatives. The mother's mother derives fitness from all such investments, whereas her mother-in-law gains only from further investment in children sired by her son, and thus has less incentive to assist the mother even if paternity is certain. This logic extends to kin other than grandmothers. We generate several hypotheses for future research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it