Modeling cultural change: Computational models of interpersonal influence dynamics can yield new insights about how cultures change, which cultures change more rapidly than others, and why.
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
Cultural change can occur as an emergent consequence of social influence dynamics within cultural populations. These influence dynamics are complex, and formal modeling methods-such as agent-based models-are a useful means of predicting implications for cultural change. These models may be especially useful if they not only model the psychological outcomes of interpersonal influence, but also model social network structures within a culture. When combined, these components provide a flexible modeling framework that allows other variables to also be modeled for the purposes of predicting plausible implications for cultural change. The article illustrates this approach by summarizing recent research that used these methods to model cross-cultural differences in the pace of cultural change. The article then identifies additional variables that could potentially be modeled within this conceptual framework, to produce additional insights-and additional new hypotheses-about different circumstances associated with different patterns of cultural change. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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