Bibliographic record
Abstract
Offspring within families, both human and nonhuman, often differ. The obvious question is: Why? Work on psychological differences on children within human families has focused primarily on differences in the nonshared environment of contemporary siblings, though the precise location of this nonshared environment is still the subject of much debate. Here I explore the range of explanations for within-brood diversity from the perspective of nonhuman families, particularly birds that share certain key features with human families. I examine the role of social rank in creating a nonshared environment within the family, and present data from a model system (an altricial bird) to illustrate how different the effective environments experienced by offspring sitting side-by-side in the same confined physical space, tended by the same parents, and experiencing similar ecological variability, can be. These broodmates can effectively live in different worlds. I then briefly explore other sources of diversity among offspring in nonhuman families, including within brood genetic differences and non-genetic maternal (parental) effects that often covary with birth / hatching rank. Given the ubiquity and far-reaching consequences of maternal effects in nonhuman families, and some human data suggestive of similar patterns, it would seem worthwhile to explore the potential role of maternal effects in creating phenotypic diversity in psychological traits among children in human families.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 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".