Méthodes d'analyse du changement fondées sur les trajectoires de développement individuelle : Modèles de régression mixtes paramétriques et non paramétriques[1]
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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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 | 0.018 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".