Informing the Classification of Positive Emotional Experiences: A Multisample Examination of Hierarchical Models of Positive Emotionality
Bibliographic record
Abstract
Despite being multifaceted in nature, positive emotional (PE) experiences often are studied using only global PE ratings, and measures assessing more specific PE facets do not converge in their assessment approaches. To address these issues, we examined hierarchical factor structures of ratings of positive emotionality, which reflect propensities toward experiencing PE, in both online community adult (N = 375) and undergraduate (N = 447) samples. Preregistered analyses indicated (a) a broad distinction between tendencies to experience social affection and other PE types, and that (b) PE ratings can be differentiated by as many as four replicable factors of Joviality, Social Affection, Serenity, and Attentiveness. These PE dimensions were associated with distinct personality and psychopathology profiles. Examples of these distinctive associations included Joviality displaying robust positive associations with grandiosity and exhibitionism; conversely, although Social Affection and Joviality were strongly correlated, Social Affection showed associations in the opposite direction with grandiosity and exhibitionism. Other notable results include Serenity (e.g., feeling relaxed) showing negative associations with negative emotionality at a magnitude indicating that Serenity may reflect low levels of negative emotionality to a considerable degree. Collectively, these results highlight the need to consider distinct PE facets in addition to global PE ratings when assessing PE, as important nuance may be lost otherwise. Furthermore, our results indicate the need for additional research clarifying PE structure at different levels of abstraction to inform future measure development efforts and assessment approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".