Improving the Meta-Analytic Assessment of Effect Size Variance With an Informed Bayesian Prior
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
Meta-analytic estimation of effect size variance is critical for determining the degree to which a relationship or finding generalizes across contexts. In most meta-analyses, population effect size variability is estimated by subtracting expected sampling error variance from observed variance, using only information from a limited set of available studies. We propose an improved Bayesian variance estimation technique that incorporates findings from previous meta-analytic research through an informed prior distribution of likely levels of effect size variance. The logic of exchangeability as a conceptual foundation for using an informed prior is explicated. On the basis of Monte Carlo simulations, we find the traditional method of meta-analytic variance estimation the most biased and least accurate technique across all sizes of meta-analyses considered. The Bayesian methodology incorporating an informed prior proved to be the most accurate and overall least biased of all estimation methods. Conceptual advantages and limitations that must be taken into account when incorporating an informed prior to estimate variability of effect sizes in a meta-analysis are also discussed.
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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 | 0.162 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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