How Do We Know What We Know? Learning from Monte Carlo Simulations
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
Monte Carlo simulations are commonly used to test the performance of estimators and models from rival methods, under a range of data-generating processes. This tool improves our understanding of the relative merits of rival methods in different contexts, such as varying sample sizes and violations of assumptions. When used, it is common to report the bias or the root mean squared error of the different methods. It is far less common to report the standard deviation, overconfidence, coverage probability, or power. Each of these six performance statistics provides important, and often differing, information regarding a method’s performance. Here, we present a structured way to think about Monte Carlo performance statistics. In replications of three prominent papers, we demonstrate the utility of our approach and provide new substantive results about the performance of rival methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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