The Relationship Between Academics’ Dark Triad Traits and Their Organizational Commitment in Physical Education and Sport
Bibliographic record
Abstract
The results of the recent studies on “dark triad” have caused this concept to become popular among the educational sciences. As the importance of these personality traits comes from the possibility of having an impact on students’ learning. Many researchers believe that teachers ‘personality traits may affect students’ learning processes in the future. So the main aim of this research is to examine the relationship between the dark triad traits of the academics and the organizational commitment dimensions. In the study conducted in the relational survey model, the Dark Triad Traits Scale developed by Paulhus & Williams (2002) and the Organizational Commitment Scale developed by Allen & Meyer (1990) were used as data collection tools. The sample of the research consists of 372 academics determined by random method. In the analysis of the data obtained from the scales, Pearson correlation coefficient was calculated to determine the level of relationship. As a result of the research, while a very low inverse relationship was determined between the emotional commitment of academics and personality traits of Machiavellianism and psychopathy, a moderate direct relationship was found between continuance commitment and the personality traits of Machiavellianism and psychopathy.
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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 | 0.005 |
| 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 | 0.000 |
| 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".