Challenges and Opportunities of Meta-Analysis in Education Research
Bibliographic record
Abstract
Meta-analyses are systematic summaries of research that use quantitative methods to find the mean effect size (standardized mean difference) for interventions. Critics of meta-analysis point out that such analyses can conflate the results of low- and high-quality studies, make improper comparisons and result in statistical noise. All these criticisms are valid for low-quality meta-analyses. However, high-quality meta-analyses correct all these problems. Critics of meta-analysis often suggest that selecting high-quality RCTs is a more valid methodology. However, education RCTs do not show consistent findings, even when all factors are controlled. Education is a social science, and variability is inevitable. Scholars who try to select the best RCTs will likely select RCTs that confirm their bias. High-quality meta-analyses offer a more transparent and rigorous model for determining best practices in education. While meta-analyses are not without limitations, they are the best tool for evaluating educational pedagogies and programs.
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.
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 | 0.757 | 0.851 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.025 | 0.019 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.025 | 0.047 |
| Open science | 0.014 | 0.023 |
| Research integrity | 0.020 | 0.039 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".