Intergenerational Incest Aversion
Bibliographic record
Abstract
The biological costs of inbreeding are expected to have shaped human incest aversion. These costs depend on biological sex, relatedness, and age. Whereas previous studies have focused on investigating how these factors modulate incest aversion in siblings and cousins—family members of the same generation—here we examined relatives of different generations. In a population-based sample, 2,499 respondents reported reactions to imagined sexual behaviors with either a biological child or parent, a niece/nephew or aunt/uncle, or a stepchild or stepparent; these responses were compared to reactions to imagined sexual behaviors involving a friend’s child or parent. Replicating prior results, women report stronger incest aversions than do men. We extend previous findings by showing that incest aversions tended to be stronger between close (vs. more distant) intergenerational relatives. Indeed, for biological relatives, decreased degree of relatedness was associated with decreased incest aversion, and for biological relatives, the certainty in relatedness was also positively associated with incest aversion. As expected, age modulated sexual aversion for unrelated, but not related, target individuals. Sexual aversions toward step-relatives did not differ from sexual aversions to biological relatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".