Modeling Individual Differences in Within-Person Variation of Negative and Positive Affect in a Mixed Effects Location Scale Model Using BUGS/JAGS
Why this work is in the frame
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Bibliographic record
Abstract
A mixed effects location scale model was used to model and explain individual differences in within-person variability of negative and positive affect across 7 days (N=178) within a measurement burst design. The data come from undergraduate university students and are pooled from a study that was repeated at two consecutive years. Individual differences in level and change in mood was modeled with a random intercept and random slope where the residual within-person variability was allowed to vary across participants. Additionally changes in within-person variability were explained by the inclusion of a time-varying predictor indicating the severity of daily stressors. This model accounted for 2 location and 2 scale effects and provided evidence that individuals who reported higher severity in daily stressors also exhibited greater variability in affect-but only for participants who showed low overall affect variability and who reported low average negative affect. Those who were more variable in their affect reports overall were less reactive to daily stressors in the sense that their high levels of affect variability remained high. We describe the utility of this model for further research on individual variation and change.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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