Replicating the Creativity-Order Tradeoff with More Conservative Time Series Models
Bibliographic record
Abstract
A new wave of research on psychology and culture uses longitudinal models and time series analyses to test questions about the relationship between culture and human behavior. In 2019, we contributed to this new literature with a paper titled “The loosening of American norms is associated with a creativity-order tradeoff” in which we found that the United States had grown less culturally “tight”—less restrictive, and more tolerant of people who break social norms—from 1800 to 2000. We also found that declines in cultural tightness—even after controlling for linear time trends, cultural collectivism, and Gross Domestic Product (GDP) per capita—were linked to higher levels of creativity (more patents, more trademarks, more feature films produced, more unorthodox baby names) and lower levels of order (higher rates of high school truancy, higher rates of adolescent pregnancy, higher levels of debt).This brief paper updates our analyses with more conservative time series models. The study of cultural change has methodologically advanced since we first conducted our analysis, and we are more aware—partly due to helpful discussions with other scholars in the field—of challenges associated with autocorrelation and underlying trends in cultural time series data. With this greater awareness, we sought to re-analyze our analysis with more conservative models for two main reasons. First, this re-analysis is important for testing whether our findings are still valid with more conservative models. Second, this procedure may help teach other researchers of cultural change about best practices when conducting time series analyses of cultural change.
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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 | 0.101 | 0.241 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".