Three Suggestions to Support Ongoing Replication Efforts: A Checklist, Visual Comparison, and Rating Scale
Bibliographic record
Abstract
We propose three practical suggestions that might be used in tandem to help address enduring replication challenges: (1) a methodology checklist to increase awareness of potentially overlooked variables and scaffold the design of replication attempts, (2) a means of facilitating visual side-by-side comparisons of reference and replication methodologies to increase replication fidelity, and (3) a broader methodology fidelity rating system to capture the nuance associated with direct replication. Incorporating these suggestions into replication efforts could facilitate improved coordination of multi-site replication attempts and offer a standardized means of assessing methodological fidelity for replication submissions and data review.
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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 | 0.771 | 0.911 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.034 | 0.020 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.016 | 0.014 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".