Self-satisfaction described by Zuckerman’s alternative five factors of personality
Bibliographic record
Abstract
BACKGROUND: This study investigates the relationships between an individual's self-satisfaction within different life areas, which correspond to Bracken's self-concept primary domains (competence, family, social, physical, emotional, and academic domains), and Zuckerman's Alternative Five-Factor Model of Personality (AFFM). It is supposed that the AFFM, as a psychobiological personality model which allows causal explanations, could provide a comprehensive insight into the nature of satisfaction with self. PARTICIPANTS AND PROCEDURE: The study included 489 adults (64% women), between 18 and 60 years old, who completed the short Self-Satisfaction Scale (SC-6) and the Zuckerman-Kuhlman-Aluja Personality Questionnaire (ZKA-PQ). Relations between personality factors and facets from the ZKA-PQ and self-satisfaction measures were analyzed using correlational and multiple regression analysis. The relationship of self-satisfaction with gender and age was also analyzed. RESULTS: Based on responses to scales, 11% to 43% of the variance in self-satisfaction responses was predicted by personality, age, and sex. Extraversion had positive predictive weights for each self-satisfaction dimension. Neuroticism had negative predictive weights which were significant except for satisfaction with family. Sensation seeking negatively predicted satisfaction with competencies, family, academic aspects, emotions, and overall satisfaction. Aggression had small negative predictor weights for satisfaction with social and academic aspects. Activity had significant positive predictor weights for competencies, academic aspects, emotions, and overall satisfaction. CONCLUSIONS: The findings suggest that personality predicts satisfaction with aspects of the self, and that the AFFM provides an adequate theoretical framework, which includes a lower level of personality traits in the explanation of the nature of a person's satisfaction, in general or related to specific life contexts.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".