Examining cultural drifts in artworks through history and development: cultural comparisons between Japanese and western landscape paintings and drawings
Bibliographic record
Abstract
Research on cultural products suggest that there are substantial cultural variations between East Asian and European landscape masterpieces and contemporary members' landscape artwork (Masuda et al., 2008c), and that these cultural differences in drawing styles emerge around the age of 8 (Senzaki et al., 2014b). However, culture is not static. To explore the dynamics of historical and ontogenetic influence on artistic expressions, we examined (1) 17-20th century Japanese and Western landscape masterpieces, and (2) cross-sectional adolescent data in landscape artworks alongside previous findings of elementary school-aged children, and undergraduates. The results showed cultural variations in artworks and masterpieces as well as substantial "cultural drifts" (Herskovits, 1948) where at certain time periods in history and in development, people's expressions deviated from culturally default patterns but occasionally returned to its previous state. The bidirectional influence of culture and implications for furthering the discipline of cultural psychology will be discussed.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".