Intellectual Capital Development Model of Muhammadiyah Higher Education in South Sulawesi
Bibliographic record
Abstract
The dynamics of human resource development is characterized by the emergence of the issue of the workers diversity that occurs in an organization or institution. Diversity is defined as the difference in competence and individual attributes among lecturers within an institution. So with the issue , the objective of the research is Human Resource performance development based on Intellectual Capital. While the specific goals to be achieved to identify, analyze, and prove the contribution of intellectual capital development model as a process in supporting the performance of caturdharma lecturer of Muhammadiyah University (PTM) in South Sulawesi.The research population is Lecturer of Muhammadiyah Universities in South Sulawesi with 158 lecturers selected by multistage sampling technique by using the method of descriptive statistic and Structural Equation Model (SEM).The research found that the intellectual capital development model of Muhammadiyah university lecturer (PTM) in South Sulawesi was done through formal education method in the form of further study, non-formal education in the form of training, and education of Al-Islam and Kemuhammadiyahan values in the form of Baitul Arqam. The intellectual capital development model of lecturers through formal education, non-formal education, and Al-Islam and Kemuhammadiyahan values education has not directly contributed to improving the performance of the tridharma of higher education.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".