Genetic Influences on Educational Achievement in Cross-National Perspective
Bibliographic record
Abstract
Abstract There is a growing interest in how social conditions moderate genetic influences on education [gene–environment interactions (GxE)]. Previous research has focused on the family, specifically parents’ social background, and has neglected the institutional environment. To assess the impact of macro-level influences, we compare genetic influences on educational achievement and their social stratification across Germany, Norway, Sweden, and the United States. We combine well-established GxE-conceptualizations with the comparative stratification literature and propose that educational systems and welfare-state regimes affect the realization of genetic potential. We analyse population-representative survey data on twins (Germany and the United States) and twin registers (Norway and Sweden), and estimate genetically sensitive variance decomposition models. Our comparative design yields three main findings. First, Germany stands out with comparatively weak genetic influences on educational achievement suggesting that early tracking limits the realization thereof. Second, in the United States genetic influences are comparatively strong and similar in size compared to the Nordic countries. Third, in Sweden genetic influences are stronger among disadvantaged families supporting the expectation that challenging and uncertain circumstances promote genetic expression. This ideosyncratic finding must be related to features of Swedish social institutions or welfare-state arrangements that are not found in otherwise similar countries.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".