Personality differences in the occurrence and affective correlates of daily positive events
Bibliographic record
Abstract
OBJECTIVE: Previous research shows that Neuroticism predicts exposure and affective reactivity to daily stressors. Zautra and colleagues extended this work to daily positive events. Building on these frameworks, we examined the Big Five personality traits as predictors of the occurrence and affective correlates of daily positive events. METHOD: Participants in two national U.S. daily diary studies (NSDE 2: N = 1,919 and NSDE Refresher: N = 778; aged 25-84) reported daily positive events, emotions specific to the events, and daily affect for 8 consecutive days. RESULTS: In parallel analyses in both samples, Extraversion and in the NSDE Refresher sample only Openness (but not Neuroticism, Conscientiousness, or Agreeableness) predicted more frequent positive event occurrence. All Big Five traits were associated with one or more emotional experiences (e.g., calm, proud) during positive events. Neuroticism predicted greater event-related positive affect in the NSDE 2 sample, whereas Agreeableness was related to more event-related negative affect in the NSDE Refresher sample. CONCLUSIONS: The Big Five personality traits each provided unique information for predicting positive events in daily life. The discussion centers on potential explanations and implications for advancing the understanding of individual differences that contribute to engagement in positive experiences.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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 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".