Interpreting prediction intervals and distributions for decoding biological generality in meta-analyses
Bibliographic record
Abstract
Abstract Despite the importance of identifying predictable regularities for knowledge transfer across contexts, the generality of ecological and evolutionary findings is yet to be systematically quantified. We present the first large-scale evaluation of generality using new metrics. By focusing on biologically relevant study levels, we show that generalization is not uncommon. Overall, 20% of meta-analyses will produce a non-zero effect 95% of the time in future replication studies with a 70% probability of observing meaningful effects in study-level contexts. We argue that the misconception that generalization is exceedingly rare is due to conflating within-study and between-study variances in ecological and evolutionary meta-analyses, which results from focusing too much on total heterogeneity (the sum of within-study and between-study variances). We encourage using our proposed approach to elucidate general patterns underpinning ecological and evolutionary phenomena.
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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 | 0.315 | 0.717 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".