Sources of Structure: Genetic, Environmental, and Artifactual Influences on the Covariation of Personality Traits
Bibliographic record
Abstract
The phenotypic structure of personality traits has been well described, but it has not yet been explained causally. Behavior genetic covariance analyses can identify the underlying causes of phenotypic structure; previous behavior genetic research has suggested that the effects from both genetic and nonshared environmental influences mirror the phenotype. However, nonshared environmental effects are usually estimated as a residualterm that may also include systematic bias, such as that introduced by implicit personality theory. To reduce that bias, we supplemented data from Canadian and German twin studies with cross-observer correlations on the Revised NEO Personality Inventory. The hypothesized five-factor structure was found in both the phenotypic and genetic/familial covariances. When the residual covariance was decomposed into true nonshared environmental influences and method bias, only the latter showed the five-factor structure. True nonshared environmental influences are not structured as genetic influences are, although there was some suggestion that they do affect two personality dimensions, Conscientiousness and Love. These data reaffirm the value of behavior genetic analyses for research on the underlying causes of personality traits.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".