Formalized synthesis opportunities for ecology: systematic reviews and meta-analyses
Bibliographic record
Abstract
Narrative reviews are dead. Long live systematic reviews (and meta-analyses). Synthesis in many forms is now a driving force in ecology. Advances in open big data for ecology and new tools provide vastly improved capacity for novel, emergent knowledge synthesis in our discipline. Systematic reviews and meta-analyses are two formal synthesis opportunities for ecologists. To date, systematic reviews are rarely used whilst the rate of meta-analyses published in ecological journals is increasing exponentially. Systematic reviews provide an overview of the literature landscape for a topic, and meta-analyses examine the strength of evidence integrated across different studies. Effective synthesis benefits from both approaches, but better data reporting and more rapid changes in the culture of data sharing will further energize these efforts. At this junction, synthetic efforts that include systematic reviews and meta-analyses should continue as stand-alone publications. This is a necessary step in the evolution of synthesis in our discipline. Nonetheless, they are still evolving tools, and meta-analyses in particular are simply an extended set of statistical tests. Admittedly, understanding the statistics and assumptions influence how we conduct synthesis much as statistical choices often shape experimental design, i.e. ANOVA versus regression-based experiments, but statistics do not make the paper. Clear ideas and excellent questions do. Titles with ‘a meta-analysis of…’ in them may have their days numbered as these tools become more widely adopted and as we seek to integrate evidence across many scales not just between studies. Approaches associated with both sets will inevitably and appropriately also become routine mechanisms to contrast primary study-level research to the work of others. For instance, in the Introduction of a primary study, formal systematic review techniques are applied, the authors identify a gap that they proceed to examine within that particular study via an experiment, and in the Discussion, the strength of the evidence is linked to other effect size estimates reported in the literature. This is already occurring but will certainly become more frequent. First steps, primary research articles need to more effectively report evidence, we need to continue to share data, and systematic reviews and meta-analyses should be used to identify research gaps and examine patterns in evidence to further predictive ecology.
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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 | 0.402 | 0.586 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.031 | 0.017 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".