Two-Part Modeling of Semicontinuous Longitudinal Variables
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents specification of two approaches for analyzing a longitudinally observed semicontinuous variable, in which a large proportion of observations equal zero while remaining observations follow a continuous distribution. Both approaches utilize two-part models, where Part 1 models the zero values, such that hypotheses can be examined regarding the likelihood that the outcome equals zero at a particular time point (using the Olsen & Schafer model) or regarding the likelihood of observing the initial onset of a nonzero value (using the “launch model”) and Part 2 for the remaining continuous portion of the outcome variable is a standard latent growth curve model. However, interpretation of Part 2 depends on the approach used in Part 1. Parts 1 and 2 are jointly estimated, allowing them to be correlated, and covariates may have differing relationships across the two parts. The approaches are illustrated using a longitudinal study of adolescent alcohol use.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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