Human Resource Management in Islamic Educational Institutions to Improve Competitiveness in Society 5.0 Era
Bibliographic record
Abstract
The ability of human resources to utilize technology in each of their activities is a competency needed in the era of society 5.0. Meanwhile, Islamic educational institutions in Indonesia in general are slow in responding to changes and developments, so the readiness of human resources in Islamic educational institutions in welcoming the era of society 5.0 is very worrying. Therefore, this study aims to reveal how human resource management is carried out in Islamic educational institutions in Indonesia. The study was conducted using qualitative methods. Data were collected from three types of Islamic educational institutions, namely traditional, modern and integrated educational institutions. These three types of institutions represent all types of Islamic educational institutions in Indonesia. Data were collected by observation, interviews, as well as documentation studies. Data were analyzed through interactive techniques. Based on the results, first, traditional Islamic educational institutions have an istiqamah attitude in carrying out existing and natural management, so as not to give special treatment to human resources in welcoming the era of society 5.0. Second, modern Islamic educational institutions apply TQM in preparing human resources to welcome the era of society 5.0. Third, integrated Islamic educational institutions accommodate the changes while still relying on instilling student morals. These three types of Islamic educational institutions have different responses in welcoming the era of society 5.0 showing the existence of Islamic educational institutions to welcome the era.
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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 | 0.003 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".