The challenge of diversity in psychology: WEIRD research, implications, and improvements
Bibliographic record
Abstract
Psychology suffers from the problem of studying a narrow database: most research is conducted on samples that are from Western, Educated, Industrialized, Rich, and Democratic societies. The problem is both that many psychological phenomena appear differently across cultures, and that WEIRD samples are psychological outliers on many dimensions. I will review some evidence that reveals the extent of cultural diversity in various psychological processes, and will discuss some implications. In particular, the WEIRD people problem intersects with a recent concern of a replicability crisis in psychology because failed replications conducted in other cultures might indicate boundary conditions for an effect, rather than a problem with internal validity. Moreover, as one solution to the replicability crisis is to collect larger sample sizes this has the unwanted consequence of further incentivizing the reliance on cheap convenience samples, which would exacerbate the WEIRD people problem. Implications and recommendations will be discussed.
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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 | 0.413 | 0.500 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.010 | 0.065 |
| Scholarly communication | 0.019 | 0.074 |
| Open science | 0.010 | 0.023 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".