Social Structure, Infectious Diseases, Disasters, Secularism, and Cultural Change in America
Bibliographic record
Abstract
Why do cultures change? The present work examined cultural change in eight cultural-level markers, or correlates, of individualism in the United States, all of which increased over the course of the 20th century: frequency of individualist themes in books, preference for uniqueness in baby naming, frequency of single-child relative to multichild families, frequency of single-generation relative to multigeneration households, percentage of adults and percentage of older adults living alone, small family size, and divorce rates (relative to marriage rates). We tested five key hypotheses regarding cultural change in individualism-collectivism. As predicted by previous theories, changes in socioeconomic structure, pathogen prevalence, and secularism accompanied changes in individualism averaged across all measures. The relationship with changes in individualism was less robust for urbanization. Contrary to previous theories, changes in individualism were positively (as opposed to negatively) related to the frequency of disasters. Time-lagged analyses suggested that only socioeconomic structure had a robust effect on individualism; changes in socioeconomic structure preceded changes in individualism. Implications for anthropology, psychology, and sociology are discussed.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".