Modelling Behaviour with Multivariate Multilevel Growth Curves
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
Abstract. Developmental data are often both longitudinal and multivariate and can be handled within a multilevel framework. This paper fits a range of multivariate multilevel models to both continuous and binary data to examine the ways in which a set of behavioural measures change together with age. These data were collected by researchers in Montreal, Canada. Methodologically, we find that within and between individual correlations vary only a little according to the ways in which the models are specified. Substantively, we find that measures of aggression and opposition are closely related but both are less closely related to a measure of hyperactivity. Models for the effects of socio-economic status on levels and changes are fitted, as are models that examine change conditional on an initial measure. The findings are compared with those previously obtained using a growth trajectories approach.
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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 | 0.005 | 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 | 0.000 | 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