Z-curve 2.0: Estimating Replication Rates and Discovery Rates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Selection for statistical significance is a well-known factor that distorts the published literature and challenges the cumulative progress in science. Recent replication failures have fueled concerns that many published results are false-positives. Brunner and Schimmack (2020) developed z-curve, a method for estimating the expected replication rate (ERR) – the predicted success rate of exact replication studies based on the mean power after selection for significance. This article introduces an extension of this method, z-curve 2.0. The main extension is an estimate of the expected discovery rate (EDR) – the estimate of a proportion that the reported statistically significant results constitute from all conducted statistical tests. This information can be used to detect and quantify the amount of selection bias by comparing the EDR to the observed discovery rate (ODR; observed proportion of statistically significant results). In addition, we examined the performance of bootstrapped confidence intervals in simulation studies. Based on these results, we created robust confidence intervals with good coverage across a wide range of scenarios to provide information about the uncertainty in EDR and ERR estimates. We implemented the method in the zcurve R package (Bartoš & Schimmack, 2020).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it