An Assessment of the Magnitude of Effect Sizes
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
This study compiles information from more than 250 meta-analyses conducted over the past 30 years to assess the magnitude of reported effect sizes in the organizational behavior (OB)/human resources (HR) literatures. Our analysis revealed an average uncorrected effect of r = .227 and an average corrected effect of ρ = .278 ( SDρ = .140). Based on the distribution of effect sizes we report, Cohen’s effect size benchmarks are not appropriate for use in OB/HR research as they overestimate the actual breakpoints between small, medium, and large effects. We also assessed the average statistical power reported in meta-analytic conclusions and found substantial evidence that the majority of primary studies in the management literature are statistically underpowered. Finally, we investigated the impact of the file drawer problem in meta-analyses and our findings indicate that the file drawer problem is not a significant concern for meta-analysts. We conclude by discussing various implications of this study for OB/HR researchers.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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