The Importance of Leadership Education in University: Self-Leadership Example
Bibliographic record
Abstract
Self-leadership is a form of leadership that has emerged in the last quarter of a century. The purpose of this study is to determine whether there is a difference in self-leadership strategies between students who choose leadership course and do not choose. The sample of this research consisted of 144 sports management students in 2018; 35 female (24.3%) and 109 male (75.7%). The average age of students is 22.38 (sd=2.88). While 30 students (20.8%) stated that they chose leadership course, 114 students (79.2%) stated that they did not choose leadership course. In this study, the Turkish version of Abbreviated Self-Leadership Questionnaire (ASLQ) was used as a data collection tool, but original ASLQ was developed by Houghton et al. (2012). The Turkish version of the scale was adopted by Şahin (2015). As a result of the reliability analysis, the Cronbach’s alpha value was found to be .74. There was a significant difference between ASLQ total scores of students who choose the leadership course and do not choose (yes/no). There was a significant difference between students who choose the leadership course and do not choose (yes/no) and the subscale scores; behavior awareness and volition, constructive cognition, and task motivation. According to the results obtained through the analysis, hypothesis 1 and 2 were accepted.
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 | 0.001 | 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 | 0.001 |
| 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".