What I experienced yesterday is who I am today: Relationship of work motivations and behaviors to within-individual variation in the five-factor model of personality.
Bibliographic record
Abstract
Historically, organizational and personality psychologists have ignored within-individual variation in personality across situations or have treated it as measurement error. However, we conducted a 10-day experience sampling study consistent with whole trait theory (Fleeson, 2012), which conceptualizes personality as a system of stable tendencies and patterns of intraindividual variation along the dimensions of the Big Five personality traits (Costa & McCrae, 1992). The study examined whether (a) internal events (i.e., motivation), performance episodes, and interpersonal experiences at work predict deviations from central tendencies in trait-relevant behavior, affect, and cognition (i.e., state personality), and (b) there are individual differences in responsiveness to work experiences. Results revealed that personality at work exhibited both stability and variation within individuals. Trait measures predicted average levels of trait manifestation in daily behavior at work, whereas daily work experiences (i.e., organizational citizenship, interpersonal conflict, and motivation) predicted deviations from baseline tendencies. Additionally, correlations of neuroticism with standard deviations in the daily personality variables suggest that, although work experiences influence state personality, people higher in neuroticism exhibit higher levels of intraindividual variation in personality than do those who are more emotionally stable.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".