Crisis? What crisis? Cross-cultural psychology’s appropriation of cultural psychology
Bibliographic record
Abstract
Whereas cross-cultural psychology and cultural psychology have been distinguished as separate projects for decades, talk about their possible collaboration is becoming increasingly common. Several scholars have described their differences as essentially non-oppositional and the latest Handbook of Cultural Psychology combines articles from both research traditions. This paper scrutinizes these consolidating efforts first by tracing historically how the two accounts of culture (cultural and cross-cultural) developed, and second, by examining whether their long-standing epistemological premises allow for the kind of collaboration advocated by some scholars. We argue that attempts to combine the disciplines come primarily from cross-cultural psychologists who appear increasingly challenged by cultural and indigenous psychological approaches. Attempts at a merger have been twofold: on the one hand, cross-cultural psychologists who seek to preserve the status of their discipline have expanded its scope to include cultural theorists; on the other hand, cross-cultural scholars persuaded by cultural theories are creating a new blend of ‘experimental cultural psychology’ that seeks to accommodate both programs. These proposals, in our view, exemplify a cross-cultural discipline in crisis, struggling to account for a growing cultural psychology. We conclude that the overlapping interests between cross-cultural and cultural scholars make this a propitious time for cross-disciplinary dialogue.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.106 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".