Genes, psychological traits and civic engagement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Civic engagement is a classic example of a collective action problem: while civic participation improves life in the community as a whole, it is individually costly and thus there is an incentive to free ride on the actions of others. Yet, we observe significant inter-individual variation in the degree to which people are in fact civically engaged. Early accounts reconciling the theoretical prediction with empirical reality focused either on variation in individuals' material resources or their attitudes, but recent work has turned to genetic differences between individuals. We show an underlying genetic contribution to an index of civic engagement (0.41), as well as for the individual acts of engagement of volunteering for community or public service activities (0.33), regularly contributing to charitable causes (0.28) and voting in elections (0.27). There are closer genetic relationships between donating and the other two activities; volunteering and voting are not genetically correlated. Further, we show that most of the correlation between civic engagement and both positive emotionality and verbal IQ can be attributed to genes that affect both traits. These results enrich our understanding of the way in which genetic variation may influence the wide range of collective action problems that individuals face in modern community life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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 it