Heritability of Intraindividual Mean and Variability of Positive and Negative Affect
Bibliographic record
Abstract
Positive affect (e.g., attentiveness) and negative affect (e.g., upset) fluctuate over time. We examined genetic influences on interindividual differences in the day-to-day variability of affect (i.e., ups and downs) and in average affect over the duration of a month. Once a day, 17-year-old twins in the United Kingdom ( N = 447) rated their positive and negative affect online. The mean and standard deviation of each individual's daily ratings across the month were used as the measures of that individual's average affect and variability of affect. Analyses revealed that the average of negative affect was significantly heritable (.53), but the average of positive affect was not; instead, the latter showed significant shared environmental influences (.42). Fluctuations across the month were significantly heritable for both negative affect (.54) and positive affect (.34). The findings support the two-factor theory of affect, which posits that positive affect is more situational and negative affect is more dispositional.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".