Coordinated distributed experiments in ecology do not consistently reduce heterogeneity in effect size
Bibliographic record
Abstract
Ecological meta‐analyses usually exhibit high relative heterogeneity of effect size: most among‐study variation in effect size represents true variation in mean effect size, rather than sampling error. This heterogeneity arises from both methodological and ecological sources. Methodological heterogeneity is a nuisance that complicates the interpretation of data syntheses. One way to reduce methodological heterogeneity is via coordinated distributed experiments, in which investigators conduct the same experiment at different sites, using the same methods. We tested whether coordinated distributed experiments in ecology exhibit 1) low heterogeneity in effect size, and 2) lower heterogeneity than meta‐analyses, using data on 17 effects from eight coordinated distributed experiments, and 406 meta‐analyses. Consistent with our expectations, among‐site heterogeneity typically comprised <50% of the variance in effect size in distributed experiments. In contrast, heterogeneity within and among studies typically comprised >90% of the variance in effect size in meta‐analyses. However, this difference largely reflected the small size of most coordinated distributed experiments, and was no longer significant after controlling for size (number of studies or sites). These results are consistent with the hypothesis that methodological heterogeneity rarely comprises a substantial fraction of variance in effect size in ecology. We also conducted pairwise comparisons of absolute heterogeneity between coordinated distributed experiments and meta‐analyses on the same topics. Coordinated distributed experiments did not consistently exhibit lower absolute heterogeneity in effect size than meta‐analyses on the same topics. Our findings suggest that coordinated distributed experiments rarely increase uniformity of results by reducing methodological heterogeneity. Our results help refine the numerous distinct reasons for conducting coordinated distributed experiments.
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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 | 0.000 |
| 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) | 0.023 | 0.002 |
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; both teacher heads agree on what is shown here.
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".