The Role of Smart Working in Mediating Participatory Altruistic Leadership, Competence, Quality knowledge in Learning Performance of Lecturers in Higher Education
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.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".