Research in culture and psychology: past lessons and future challenges
Bibliographic record
Abstract
Since the dawn of psychology as a science, conceptual and methodological questions have accompanied research at the intersection of culture and psychology. We review some of these questions using two dominant concepts-independent versus interdependent social orientation and analytic versus holistic cognitive style. Studying the relationship between culture and psychology can be difficult due to sampling restrictions and response biases. Since these challenges have been mastered, a wealth of research has accumulated on how culture influences cognition, emotion, and the self. Building on this work, we outline a set of new challenges for culture and psychology. Such challenges include questions about conceptual clarity, within-cultural and subcultural variations (e.g., variations due to social class), differentiation and integration of processes at the group versus individual level of analysis, modeling of how cultural processes unfold over time, and integration of insights from etic and emic methodological approaches. We review emerging work addressing these challenges, proposing that future research on culture and psychology is more exciting than ever. WIREs Cogn Sci 2014, 5:1-14. doi: 10.1002/wcs.1267 CONFLICT OF INTEREST: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website.
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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 | 0.066 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.016 | 0.054 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".