The Role of Smart Working in Mediating Participatory Altruistic Leadership, Competence, Quality knowledge in Learning Performance of Lecturers in Higher Education
Why this work is in the frame
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Bibliographic record
Abstract
This study aims to explore indicate that lecturer learning performance is an important factor for lecturers in the Civil Engineering Education Study Program. Participatory altruistic leadership styles, competence, quality knowledge, smart working are very important to be measured to explain their effects on learning performance. If the lecturer has high competence and quality knowledge and is supported by appropriate leadership, it will have an impact on smart working, which in the end will achieve learning performance. This research was conducted of lecturers of civil engineering education throughout Indonesia and a sample of 76 peoples. The research method was conducted with quantitative and data analysis using structural equation modeling (SEM). Regression coefficient result that relationship between competence with smart working, participatory altruistic with smart working and quality of knowledge with smart working were 0.80, 0.86 and 0.81. Regression coefficient result that relationship between smart woking with learning performance, participatory altruistic with learning performance, quality knowledge with smart working and competence with smart working were 0.99, 0.91, 0.88, and 0.88.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it