Reliability of meta-analyses in ecology and evolution: (mostly) good news from a case study on sexual signals
Bibliographic record
Abstract
Meta-analyses are powerful synthesis tools that are popular in ecology and evolution owing to the rapidly growing literature of this field. Although the usefulness of meta-analyses depends on their reliability, such as the precision of individual and mean effect sizes, attempts to reproduce meta-analyses' results remain rare in ecology and evolution. Here, we assess the reliability of 41 meta-analyses on sexual signals by evaluating the reproducibility and replicability of their results. We attempted to: (i) reproduce meta-analyses' mean effect sizes using the datasets they provided; (ii) reproduce meta-analyses' effect sizes by re-extracting 5703 effect sizes from 246 primary studies they used as sources; (iii) assess the extent of relevant data missed by original meta-analyses; and (iv) replicate meta-analyses' mean effect sizes after incorporating re-extracted and relevant missing data. We found many discrepancies between meta-analyses' reported results and those generated by our analyses for all reproducibility and replicability attempts. Nonetheless, we argue that the meta-analyses we evaluated are largely reproducible and replicable because the differences we found were small in magnitude, leaving the original interpretation of these meta-analyses' results unchanged. Still, we highlight issues we observed in these meta-analyses that affected their reliability, providing recommendations to ameliorate them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.562 | 0.886 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".