DETERMINING THE RELATIONSHIP BETWEEN ALEXITIMIC PERSONALITY FEATURES AND NARSISTIC PERSONALITY FEATURES OF PHYSICAL EDUCATION AND SPORTS SCHOOL STUDENTS (Mardin Province Example)
Bibliographic record
Abstract
The aim of this study is to determine the relationship between alexithymic personality traits and narcissistic personality traits of students studying at Mardin Artuklu University School of Physical Education and Sports. In the study, Toronto Alexithymia Short Version (TAS-20) and narcissistic personality inventory were used as data collection tools. The relational model, which is included in the quantitative research method, was used in the research. The sample of the study consists of 154 students studying at Mardin Artuklu University School of Physical Education and Sports. SPSS 20 program was used in the analysis of the data obtained. In the analysis of the obtained data, firstly homogeneity and normality distribution were examined. For this, Kolmogorov-Smirnov (K-S) and Shapiro Wilks tests were used. After this review, it was decided to use the parametric test method in the analysis of the data. Descriptive statistics and Pearson correlation analysis were used in the analysis of the data. At the end of the correlation analysis, a statistically significant positive correlation was found between the students' narcissistic personality levels and total alexithymia level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".