META ANALYSES OF CORRELATED MULTIPLE BASELINE TIME SERIES DESIGN INTERVENTION MODELS USING JR ESTIMATE
Bibliographic record
Abstract
This study develops R estimators of the fixed effects in an experiment done over correlated baseline series.Besides a simple parametric approach we investigate linear mixed models procedures also.The random errors are independent within OLUWAGBOHUNMI AWOSOGA et al. 132 series but are dependent between several baseline series.The JR method seems more appropriate in term of its empirical type I error and the power of the test for the case of independence within but dependence between series than other methods (i.e., CT, WW, and LME) considered in this study.We illustrated the robustness of the procedures on a real data set which contained some outliers.Our robust procedures were much less sensitive to the effect of the outliers than the traditional analysis based on LS.A simulation study over situations similar to that of the data set confirmed the validity of our new approaches.The study also showed the robustness of efficiency of our approach over that of the traditional analysis.
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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 | 0.125 | 0.251 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.024 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".