How to critically read ecological meta-analyses.
Bibliographic record
Abstract
Meta-analysis offers ecologists a powerful tool for knowledge synthesis. Albeit a form of review, it also shares many similarities with primary empirical research. Consequently, critical reading of meta-analyses incorporates criteria from both sets of approaches particularly because ecology is a discipline that embraces heterogeneity and broad methodologies. The most important issues in critically assessing a meta-analysis initially include transparency, replicability, and clear statement of purpose by the authors. Specific to ecology more so than other disciplines, tests of the same hypothesis are generally conducted at different study sites, have variable ecological contexts (i.e., seasonality), and use very different methods. Clear reporting and careful examination of heterogeneity in ecological meta-analyses is thus crucial. Ecologists often also test similar hypotheses with different species, and in these meta-analyses, the reader should expect exploration of phylogenetic dependencies. Finally, observational studies not only provide the substrate for potential current manipulative experiments in this discipline but also form an important body of literature historically for synthesis. Sensitivity analyses of observational versus manipulative experiments when aggregated in the same ecological meta-analysis are also frequent and appropriate. This brief conceptual review is not intended as an instrument to rate meta-analyses for ecologists but does provide the appropriate framing for those purposes and directs the reader to ongoing developments in this direction in other disciplines.
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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 | 0.107 | 0.489 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.037 | 0.016 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".