An Integrated Ecological Approach to Mapping Variations in Collectivism Within China: Introducing the Triple-Line Framework
Bibliographic record
Abstract
Measurable regional variations in collectivism have been found across the Chinese mainland, challenging the simple classification of China as a “collectivistic society” in cross-national studies. In previous studies, a small number of distal or proximal ecological factors have been used to explain these regional variations of collectivism. However, there has been little consensus on which ecological factors best predict regional collectivism. In this article, the authors propose the “triple-line framework,” an integrated perspective on regional variations in collectivism. This framework divides China into four regions using three lines—the Hu Huanyong Line, the Great Wall Line, and the Qinling–Huaihe Line—according to their ecological, historical, and social characteristics. A growing body of empirical research is largely consistent with this framework. The authors conclude by discussing the potential for this framework to generate new, testable hypotheses and consider some ways in which this approach to intranational variation could be used by cultural psychologists working in other parts of the world.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".