Best practices for implementing equitable and just large-scale randomized trials in majority world countries.
Bibliographic record
Abstract
This article focuses on a widely used method in developmental and education research in majority world countries: large-scale impact evaluations and randomized controlled trials. We build on our experience implementing such programs in majority world countries, primarily in West Africa, and reflect on our experiences to propose a set of best practices in maintaining equity and justice in collaborations between majority and minority world countries. These include prioritizing research-policy-community partnerships, promoting complementary research approaches, considering measurement timescales, and capacity exchange approaches. (PsycInfo Database Record (c) 2026 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 | 0.795 | 0.867 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".