A Problem in Theory and More: Measuring the Moderating Role of Culture in Many Labs 2
Bibliographic record
Abstract
The multi-site replication study, Many Labs 2 (ML2), attempted to test whether population, site and setting variability moderates the likelihood of replication and effect size. The analysis concluded that sample location and setting did not substantially affect the replicability of findings. In this paper, we raise several issues with the ML2 approach to adjudicating the effect of culture that cast doubt on this conclusion. These theoretical and methodological problems (pre-registered at https://osf.io/6exr4) involve the: (1) selection of studies and sample sites for replication that are not theory-driven, (2) sampling of mostly WEIRD people around the world, (3) conflation of participants’ cultural backgrounds with the country where the samples came from, (4) use of the WEIRD backronym by decomposing it into a scale, and (5) application of a mean split of that WEIRD variable. Moreover, simulations reveal strikingly low statistical power for detecting cultural influences in a multi-side study designed like ML2. We propose methodologies to address problems (3) to( 5) by re-analyzing the ML2 dataset using an alternative approach. These results suggest that tackling only some of the design problems is insufficient to overcome the underlying theoretical and methodological deficiencies. We conclude with specific recommendations for assessing the role of population variability in future multi-site studies that address evidentiary value and effect size.
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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 | 0.555 | 0.764 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".