A class of flexible models for analysis of complex structured correlated data with application to clustered longitudinal data
Why this work is in the frame
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Bibliographic record
Abstract
Generalized linear mixed models have been widely used in correlated data analysis. The applicability of these models is, however, hampered when data possess multilevel complex association structures. For instance, for longitudinal data arising in clusters, modelling complexity is a serious issue, and it is desirable to develop flexible models that are both computationally manageable and interpretatively meaningful. For these purposes, we propose a new class of flexible models, pairwise generalized linear mixed models, to facilitate correlated data that may possess multilevel complex association structures. Inferential procedures are developed to accommodate the proposed modelling framework, and asymptotic properties of the proposed method are established. The proposed models are evaluated through numerical studies. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.001 | 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