Indonesian Private University Lecturer Performance Improvement Model to Improve a Sustainable Organization Performance
Bibliographic record
Abstract
Lecturer performance is related to the quality of educators that are owned by a university. Poor performance will affect the quality and carrying capacity of the sustainability of an organization, in this case the university. There are many models developed to measure the performance of teachers, but not much to discuss the influence of faculty performance itself towards sustainability of an organization that houses the professor. This study was conducted in an attempt to measure the performance of lecturers to support the sustainability of the university. A faculty performance assessment model is developed to see whether the performance of lecturers affect the sustainability of the university. The method used is descriptive and verification of data sample of 275 private university lecturers in Banten Province. Application of Structural Equation Model (SEM) was used to test the model and estimate hypothesis using LISREL Results showed that leadership, motivation, job satisfaction, and organizational commitment affect positively and significantly to the performance of lecturers either partially or together with a contribution of 84%. The perception of organizational commitment variable was high which is good. In addition, responsibility for the improvement of organizational performance based on indicators of commitment to the organization ranked highest score of the respondent's perception. This showed that the lecturers were highly committed to the organization which is a great asset in the process of sustainability of an organization. In addition to the score of a continuing commitment to the organization shows lecturers should continue to be fostered in order to maintain the quality of organizational performance. This study shows that the performance of lecturers can support the sustainability of the organization
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".