In-Law Relationships in Evolutionary Perspective: The Good, the Bad, and the Ugly
Bibliographic record
Abstract
In-laws (relatives by marriage) are true kin because the descendants that they have in common make them "vehicles" of one another's inclusive fitness. From this shared interest flows cooperation and mutual valuation: the good side of in-law relationships. But there is also a bad side. Recent theoretical models err when they equate the inclusive fitness value of corresponding pairs of genetic and affinal (marital) relatives-brother and brother-in-law, daughter and daughter-in-law-partly because a genetic relative's reproduction always replicates ego's genes whereas reproduction by an affine may not, and partly because of distinct avenues for nepotism. Close genetic relatives compete, often fiercely, over familial property, but the main issues in conflict among marital relatives are different and diverse: fidelity and paternity, divorce and autonomy, and inclinations to invest in distinct natal kindreds. These conflicts can get ugly, even lethal. We present the results of a pilot study conducted in Bangladesh which suggests that heightened mortality arising from mother-in-law/daughter-in-law conflict may be a two-way street, and we urge others to replicate and extend these analyses.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".