Méthodes d'analyse du changement fondées sur les trajectoires de développement individuelle : Modèles de régression mixtes paramétriques et non paramétriques[1]
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
Longitudinal Methods Based on Individual Development Trajectories - Parametric and Non Parametric Mixed Models: Generalized linear mixed models encompass a variety of modern longitudinal analytic approaches based on individual developmental trajectories. These models overcome many important problems inherent to other traditional analysis of longitudinal data. They all rely on two basic levels: the lower one express, through a set of parameters, the individual pattem of change over time ( within-individual change), whereas the upper level captures the variations between these parameters describing individual trajectories ( between-individual differences in change). However, other characteristics distinguish différent sorts of mixed models, such as their assumptions concerning the distribution of the trajectories within the population. This introductory article presents the basic linear mixed model assuming a normal distribution of the unobserved heterogeneity, and the nonparametric mixture model that relies on a discrete approximation of the unobserved heterogeneity. Before comparing these two models, the first section of the article gives a general description of the notion of individual developmental trajectories.
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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 | 0.085 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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