Bibliographic record
Abstract
Culture arises from the interaction of many individuals sharing knowledge and collaborating over time, and, because of this, culture must be studied using different methods to those that are commonly employed in many areas of psychology. The majority of our understanding of the social learning underpinning culture is generalised from ‘dyadic’ experiments, in which a single participant observes a single model, and as a result leaves questions about the relationship between the individual- and group-level unaddressed. Such questions include how different forms of cultural information spread across the population and how individuals work together to produce cultural products. Diffusion experiments present a method for such dynamics to be examined. This chapter reviews the varieties of diffusion methods available and the strengths and weaknesses each type of diffusion design provides in answering questions about cultural evolution. It also reviews recent innovations in studying the spread of culture via social relations using social network analyses. We argue that social network analyses could be especially useful for examining a dynamic which has hitherto not been widely considered in studying cultural evolution; that is, the feedback relationship between social structure (relations) and social learning, with copying behaviour fulfilling both a role of information exchange and a role of affiliation.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".