Perspectives of researchers engaging in majority world research to promote diverse and global psychological science.
Bibliographic record
Abstract
Journal analyses have documented the historical neglect of research pertaining to the Majority World in psychological science, and the need for inclusivity is clearly articulated to ensure a science that is comprehensive and globally applicable. However, no systematic efforts have explored the perspectives of researchers working with Majority World communities regarding the challenges they experience in conducting and disseminating research and ways to address them. Our aim was to explore these challenges from the perspective of these researchers using an embedded mixed-methods design. Based on responses of 232 researchers who engage in psychological research with Majority World communities (68.1% from Africa, Asia, or Latin America, remaining from the Minority World), we identified challenges in three areas: (a) stemming from an inherent bias against Majority World research, (b) experienced by all researchers, which nonetheless are heightened for those engaging in research with Majority World populations, and (c) specific to researchers affiliated with Majority World institutions. Based on the findings, we recommend journal editorial teams and funding agencies: (a) acknowledge and address the bias inherent in the publication and funding process, (b) recruit editorial team members, program officers, and reviewers from the Majority World, (c) train editorial team members, program officers, and reviewers from the Minority World to thoughtfully evaluate Majority World research, and (d) provide resources for researchers affiliated with Majority World institutions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.218 | 0.336 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.023 | 0.026 |
| Scholarly communication | 0.039 | 0.018 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".