Perspectives of researchers engaging in majority world research to promote diverse and global psychological science.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.012 |
| Science and technology studies | 0.000 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it