Bibliographic record
Abstract
Receiver operating characteristic (ROC) curve analyses have become increasingly common in eyewitness lineup experiments, yet statistical power for these analyses is not well-understood. powe(R)OC is a free, open-source R Shiny web app that allows users with minimal programming and statistical knowledge to conduct simulation-based power analyses for two-condition (e.g., simultaneous vs. sequential lineups) eyewitness lineup experiments (and certain other recognition memory experiments) that use ROC analysis. powe(R)OC uses existing data (either user-uploaded or publicly-available open data) as a basis for simulation, allows powering for analyses of partial area under the curve (pAUC) and deviation from perfect performance (Smith et al., 2019), allows users to specify various simulation parameters (e.g., effect sizes, sample sizes, number of lineups, pAUC truncation, test tails), view simulation results uploaded by other users, view ROC effect sizes in the literature, and download simulation results in a summary report. This report describes ROC analyses, challenges in simulating ROC data, methods implemented in powe(R)OC and their underlying assumptions and limitations, and possible future directions.
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How this classification was reachedexpand
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".