An Investigation into the Correlation Between Personality Traits and Happiness Levels Among College Students.
Bibliographic record
Abstract
Objective: Drawing from Allport's (1961) definition, personality encompasses the dynamic organization of psychophysical systems within an individual, shaping characteristic patterns of thoughts, feelings, and behaviors. Happiness, as described by Courtney E. Ackerman, denotes a transient state of consciousness resulting from the attainment of personal values rather than a enduring trait. This study aimed to explore the associations between personality traits and happiness among college students, considering gender differences. Participants completed the Eysenck Personality Questionnaire Revised-Abbreviated and the Subjective Happiness Inventory (General Happiness Scale). Data analysis involved Mean, Standard Deviation, Kruskal-Wallis test, and Spearman rank correlation. Results: Findings revealed no significant correlation between personality traits and happiness levels. However, a notable gender disparity was observed in the level of psychoticism among college students. Conversely, no significant gender differences were found in neuroticism, extraversion, and happiness levels. These results suggest that personality does not serve as a determinant framework for understanding happiness dynamics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".