Accelerating the science and practice of psychology beyond WEIRD biases: Enriching the landscape through Asian psychology
Bibliographic record
Abstract
More than a decade has passed since major concerns emerged about the WEIRD-centric focus of mainstream psychological science. Since then, many calls have been made for the discipline of psychology (and other disciplines within the social sciences) to become more broadly representative of the human species. However, recent evidence suggests that progress toward improving the inclusivity and generalizability of psychological science has been slow, and that the dominance of WEIRD psychology has persisted. To build a more comprehensive psychological science that truly represents the global population, we need strategies that can facilitate more rapid expansion of empirical evidence in psychology beyond WEIRD biases. In this paper, we draw on several examples (i.e., non-duality and dialectical interaction, Wu-Wei, Zhong Yong) to illustrate how principles of Asian psychology could contribute to reshaping mainstream psychology. We discuss some strategies for advancing a global psychological science, along with some complementary practical suggestions that could enrich the WEIRD-centric landscape of current psychological science.
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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 | 0.036 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".