THE DARK TRIAD IN PERSONALITY AND ITS RELATIONSHIP WITH ALEXITHYMIA AND EMOTIONAL REGULATION DIFFICULTIES AMONG UNIVERSITY STUDENTS.
Bibliographic record
Abstract
The study aimed to explore the relationship between the Dark Triad personality traits, alexithymia, and emotional regulation difficulties among university students. The study was conducted on a sample of 258 students, who were assessed by using the Dark Triad personality scale (Adapted and standardized by Karim, 2016), the Toronto Alexithymia Scale (TAS-20) developed by Bagby et al. (1994) and translated into Arabic by Al-Eidan (2019), and the Emotional Regulation Difficulties Scale (brief version) developed by Bjureberg et al. (2016) and translated into Arabic by Abadi et al. (2019). Results shows statistically significant positive correlation between students' scores on the Dark Triad personality scale and their scores on both the alexithymia and emotional regulation difficulties scales. In addition, results found statistically significant positive correlation between students' scores on the Dark Triad personality scale and their scores on both the alexithymia and emotional regulation difficulties scales. Moreover, there was a statistically significant positive correlation found between students' scores on the Dark Triad personality scale and their scores on both the alexithymia and emotional regulation difficulties scales. Also, results illustrate the prediction of Alexithymia in students based on their scores on the Dark Triad personality traits and emotional regulation difficulties scales. Finally, there weren't statistically significant differences found between students' average scores on the Dark Triad, alexithymia, and emotional regulation difficulties scales based on gender, academic specialization, or their interaction.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".