Challenges and Opportunities for Psychological Research in the Majority World
Bibliographic record
Abstract
How can psychology transform itself into an inclusive science that engages with the rich cultural diversity of humanity? How can we strive towards a broader and deeper understanding of human behavior that is both generalizable across populations and attentive to its diversity? To address these major questions of our field, relying on scholars from different world regions, we outline first the opportunities associated with conducting psychological research in these and other majority world regions, highlighting international collaborations. Cross-cutting research themes in psychological research in the majority world are presented along with the urgent need to adopt a more critical lens to research and knowledge production within psychology. Indigenization, critical, transformative and liberatory approaches to understanding psychological phenomena framed within the decolonial imperative are presented as future options for a more diverse and equitable psychological science. Next, we address challenges, including limited institutional research infrastructure, limited national investment in research, political and social challenges these regions face, and the impact of imported (rather than locally produced) psychological knowledge. We conclude by offering recommendations to enable psychological science to be more representative of the world’s population. Our aim is to facilitate a broader, better-informed, and more empathic conversation among psychological scientists worldwide about ways to make psychological science more representative, culturally informed and inclusive.
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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 | 0.177 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.048 |
| Scholarly communication | 0.028 | 0.038 |
| Open science | 0.005 | 0.033 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 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".