University Majors and Personality Traits: A Quantitative Study of Natural Sciences and Language Majors
Bibliographic record
Abstract
This study aims to investigate the relationship between university majors and personality traits, with a specific focus on the effects of studying natural sciences mainly, chemistry, biology, and physics and humanities majoring in English and Arabic languages on personality development. The study employs a quantitative research design, using self-report questionnaires and interviews to collect data from undergraduate students majoring in natural sciences and languages. The results of the study show that natural science learners tend to score higher on analytical thinking, attention to details, and persistence skills, while language learners tend to score higher on social awareness, critical consciousness, and communicative competence. These findings suggest that university majors have a significant impact on personality traits, which also have implications for academic and career success. The theoretical framework for this study draws on the literature on cognitive and social aspects of natural sciences and languages education, as well as the theory of Big Five personality traits. The study contributes to the theoretical and practical knowledge of how academic disciplines influence individual differences in personal characteristics and provides empirical evidence for the relationship between university majors and personality traits.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".