Learning Curve: Progress in the Replication Crisis
Bibliographic record
Abstract
We present detailed monitoring data across a five-country randomized trial of phone-based targeted tutoring–one of the largest multicountry replication efforts in education to date. We study an approach shown to work in Botswana and replicated in India, Kenya, Nepal, the Philippines, and Uganda. While the existing literature often finds diminishing effects as proof-of-concept studies are replicated and scaled, we find the opposite: implementation fidelity (the degree of targeted educational instruction) improves across replications and over time. This demonstrates that replication is not intractable; rather, equipped with mechanisms to learn from experience, organizational “learning curves” can enable effective replication and scale-up.
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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 | 0.590 | 0.831 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".