Heterogeneity in ecological and evolutionary meta‐analyses: its magnitude and implications
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
Abstract Meta‐analysis is the gold standard for synthesis in ecology and evolution. Together with estimating overall effect magnitudes, meta‐analyses estimate differences between effect sizes via heterogeneity statistics. It is widely hypothesized that heterogeneity will be present in ecological/evolutionary meta‐analyses due to the system‐specific nature of biological phenomena. Despite driving recommended best practices, the generality of heterogeneity in ecological data has never been systematically reviewed. We reviewed 700 studies, finding 325 that used formal meta‐analysis, of which total heterogeneity was reported in fewer than 40%. We used second‐order meta‐analysis to collate heterogeneity statistics from 86 studies. Our analysis revealed that the median and mean heterogeneity, expressed as I 2 , are 84.67% and 91.69%, respectively. These estimates are well above “high” heterogeneity (i.e., 75%), based on widely adopted benchmarks. We encourage reporting heterogeneity in the forms of I 2 and the estimated variance components (e.g., τ 2 ) as standard practice. These statistics provide vital insights in to the degree to which effect sizes vary, and provide the statistical support for the exploration of predictors of effect‐size magnitude. Along with standard meta‐regression techniques that fit moderator variables, multi‐level models now allow partitioning of heterogeneity among correlated (e.g., phylogenetic) structures that exist within data.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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