Sexual Selection and the Treatment of Predecessors’ Progeny by Replacement Mates
Bibliographic record
Abstract
Darwin's theory of sexual selection provides a useful framework for understanding the behavior of stepparents. A non-human animal whose new mate has dependent young may kill, ignore, or adopt the predecessor's progeny. The third option has been interpreted as courtship ("mating effort"), and whether selection favors such investment over killing or ignoring the young apparently depends on aspects of the species-typical ecology and demography. The tripartite categorization of responses is a simplification, however, There is variability both within and between species along a continuum from rejection to "full adoption." The average stepparent invests less than the average birth parent, but more than nothing. Human stepparents have often been found to kill young children at higher rates than birth parents, but stepparental infanticide cannot plausibly be interpreted as a human adaptation, both because it is extremely rare and because it is almost certainly more likely to reduce the killer's fitness than to raise it. How sexual selection theory remains relevant to human stepparenting is by suggesting testable hypotheses about predictors of the variability in stepparental investment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".