A CALL FOR CAUTIOUS INTERPRETATION OF META-ANALYTIC REVIEWS
Bibliographic record
Abstract
Abstract Meta-analytic reviews collect available empirical studies on a specified domain and calculate the average effect of a factor. Educators as well as researchers exploring a new domain of inquiry may rely on the conclusions from meta-analytic reviews rather than reading multiple primary studies. This article calls for caution in this regard because the outcome of a meta-analysis is determined by how effect sizes are calculated, how factors are defined, and how studies are selected for inclusion. Three recently published meta-analyses are reexamined to illustrate these issues. The first illustrates the risk of conflating effect sizes from studies with different design features; the second illustrates problems with delineating the variable of interest, with implications for cause-effect relations; and the third illustrates the challenge of determining the eligibility of candidate studies. Replication attempts yield outcomes that differ from the three original meta-analyses, suggesting also that conclusions drawn from meta-analyses need to be interpreted cautiously.
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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 | 0.681 | 0.857 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.022 | 0.018 |
| Bibliometrics | 0.028 | 0.015 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.033 | 0.028 |
| Open science | 0.029 | 0.014 |
| Research integrity | 0.025 | 0.067 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".