A Brief Guide to Evaluate Replications
Bibliographic record
Abstract
The importance of replication is becoming increasingly appreciated, however, considerably less consensus exists about how to evaluate the design and results of replications. We make concrete recommendations on how to evaluate replications with more nuance than what is typically done currently in the literature. We highlight six study characteristics that are crucial for evaluating replications: replication method similarity, replication differences, investigator independence, method/data transparency, analytic result reproducibility, and auxiliary hypotheses’ plausibility evidence. We also recommend a more nuanced approach to statistically interpret replication results at the individual-study and meta-analytic levels, and propose clearer language to communicate replication results.
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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 | 0.256 | 0.582 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.113 | 0.063 |
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".