Development is in the details: Age differences in the Big Five domains, facets, and nuances.
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
= .65) predictions were more accurate. Less than 15% of the sample was needed to train models to their optimal accuracy. Residualizing the 300 items for all facets had no impact on their predictive accuracy, suggesting that age differences in specific behaviors, thoughts, and feelings (i.e., items) were not because of domains and facets but mostly unique to nuances. These findings replicated in a multisample dataset tested with another questionnaire. We found little evidence that age differences only appeared nuanced because items referred to age-graded roles or experiences. Therefore, a substantial part of personality development may be uniquely ascribed to narrow personality characteristics, suggesting the possibility for a many-dimensional representation of personality development. Besides theoretical implications, we provide concrete illustrations of how this can open new research avenues by enabling to study systematic variations between traits. (PsycInfo Database Record (c) 2021 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 | 0.002 | 0.000 |
| 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 | 0.001 |
| 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