Modeling individual differences in emotion regulation repertoire in daily life with multilevel latent profile analysis.
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
= 179, 9-10 prompts per day over 21 days) to (a) group the occasions into latent profiles of momentary ER strategies, (b) group individuals whose distributions of ER profiles differed across occasions into latent classes, and (c) examine well-being correlates of class membership at the person level. At the occasion level, we identified nine ER profiles that differed in degree of use (e.g., no use of any vs. strong use of all strategies) and in specific combinations of strategies (e.g., situation selection and acceptance vs. suppression and ignoring). At the person level, we identified 5 classes of individuals differing in the degree to which they used various momentary ER profiles versus one predominant profile across situations. Well-being was highest for individuals who used multiple ER profiles of active strategies and lowest for individuals who used ER profiles focused on suppression. Hence, both ER repertoire width and the specific make-up of the ER repertoire were relevant for the relation between ER repertoire and well-being. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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