A correction factor for the impact of cluster randomized sampling and its applications.
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
Cluster randomized sampling is 1 method for sampling a population. It requires recruiting subgroups of participants from the population of interest (e.g., whole classes from schools) instead of individuals solicited independently. Here, we demonstrate how clusters affect the standard error of the mean. The presence of clusters influences 2 quantities, the variance of the means and the expected variance. Ignoring clustering produces spurious statistical significance and reduces statistical power when effect sizes are moderate to large. Here, we propose a correction factor. It can be used to estimate standard errors and confidence intervals of the mean under cluster randomized sampling. This correction factor is easy to integrate into regular tests of means and effect sizes. It can also be used to determine sample size needed to reach a prespecified power. Finally, this approach is an easy-to-use alternative to linear mixed modeling and hierarchical linear modeling when there are only 2 levels and no covariates.
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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 | 0.005 | 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 | 0.000 | 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