A Roadmap to Large-Scale Multi-Country Replications in Psychology
Bibliographic record
Abstract
Classic findings from psychology and the behavioural sciences are increasingly being revisited. Methodological and technological advances provide opportunities to replicate studies across a wide range of countries and settings to investigate whether these findings are universally applicable, limited to specific countries, or vary in magnitude depending on settings. Researchers from around the world connect to revisit such findings collaboratively, adapt the original design to the Zeitgeist, integrate new knowledge to improve statistical analyses, and broaden the scope by testing effects globally – or at least in as many countries, as budget and feasibility allow. We currently observe multiple international consortia conducting large-scale multi-country replications. How do such collaborations form and how do they approach these complex investigations? This paper brings together researchers from different initiatives that conduct replications on an international scale to outline approaches and summarises what we have learned in applying them: Junior Researcher Programme (JRP), Psychological Science Accelerator (PSA), ManyBabies, Collaborative Open-science REsearch (CORE), and International Study of Metanorms (ISMN). We describe different ways for study selection, methodological approaches, statistical analyses, ethical issues, and most importantly, how the different collaborations formed and how team communication worked. We look in detail at challenges of including typically underrepresented countries in psychological science, not only in terms of data collection but also in making it possible for local researchers to contribute. This paper provides a structured insight into how different collaborations work and issues to consider for anyone who seeks to conduct a multi-country replication in psychology, or looking for additional perspectives to their existing plan. We close the article with a checklist built as a helpful tool for colleagues putting together their study protocols for such efforts – and invite them to collaboratively expand it in the future.
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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 | 0.668 | 0.700 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.021 | 0.041 |
| Open science | 0.012 | 0.037 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".