Bootstrap confidence intervals for the mean correlation corrected for Case IV range restriction: A more adequate procedure for meta-analysis.
Bibliographic record
Abstract
In this study, we proposed to use the nonparametric bootstrap procedure to construct the confidence interval for the mean correlation r corrected for Case IV range restriction in meta-analysis (i.e., ; Hunter, Schmidt, & Le, 2006). A comprehensive Monte Carlo study was conducted to evaluate the accuracy of the parametric confidence interval and 3 nonparametric bootstrap confidence intervals for r(c4). Of the 4 intervals, our results showed that the bootstrap bias-corrected and accelerated percentile interval (BCaI) yielded the most accurate results across different data situations. In addition, the mean-corrected correlation r(c4) was found to be more accurate than the uncorrected estimate. Implications of the mean-corrected correlation r(c4) and BCaI in organizational studies are also discussed.
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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 | 0.261 | 0.654 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".