A CALL FOR CAUTIOUS INTERPRETATION OF META-ANALYTIC REVIEWS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it