FAMILY FLYNN EFFECTS AND LINKS TO MIDDLE-AGE HEALTH OUTCOMES
Bibliographic record
Abstract
Abstract The Flynn effect (Flynn, 1984; 1987) refers to increases in cognitive performance, for later-born cohorts. It has been documented globally, occurring for more than a century. In a meta-analysis, Pietschnig and Voracek (2015) noted that the effect may be even stronger in adults than in children, though little research has addressed this topic (or its implications) for aging adults. Similarly, overall life-time health has improved, and incidences of cognitive impairment have decreased during the last two decades (Clouston et al., 2021). Using multilevel growth curve models, we found family Flynn effects in the National Longitudinal Survey of Youth; children in families with later-born mothers, and later-born first children, had higher PIAT math scores, and steeper developmental slopes. Although the link from childhood and adolescent cognitive function to later life outcomes has been well studied, research that takes advantage of the Flynn effect to facilitate interpreting that link is lacking. Clouston et al. (2021) emphasized the value of the Flynn effect in investigating links between childhood cognitive functioning and later adult Alzheimer’s disease and related dementia (ADRD) risks. We linked our family level results to middle-age maternal health outcomes (factors that are related to ADRD risks). Canonical correlation analyses showed that mothers (at ages 40+ and 50+) from families with higher score levels and slopes tended to have better mental and physical health. Our results, showing a Flynn effect in child and adolescence scores, at the family level, with links to adult health, persisted after controlling for a known selection bias.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".