Konsep Manajemen Insani Sebagai Upaya Peningkatan Kinerja Guru Di Madrasah
Bibliographic record
Abstract
Teachers have their own problems when it comes to deal with their performances quality they have done so far. One of the problems which is clear that some teachers do not really develop themsleves and do reflections to all their performance processes. Teachers’ performance improvement actually can be improved by two directions in human management concept, that is leader’s and colleague’s supports and motivating themselves to do many reflections. The human management concept that is studied in this article is focused on teachers’ performance improvement by maximize human potential as the first and main components to improve learning quality and education itself. Para guru memiliki problematika tersendiri ketika bersinggungan dengan kualitas kinerja yang dilakukannya. Salah satu problematika yang tampak adalah kurangnya sebagian guru dalam melakukan pengembangan diri dan refleksi terhadap keseluruhan proses kinerja. Peningkatan kinerja guru sebenarnya bisa dilakukan melalui dua arah pada konsep manajemen insani, yaitu dukungan dari pimpinan dan kolega dan memotivasi diri sendiri dengan melakukan banyak refleksi diri. Konsep manajemen insani yang dikaji pada artikel ini berfokus pada peningkatan kinerja guru dengan memaksimalkan potensi manusia sebagai komponen pertama dan utama dalam meningkatkan kualitas pembelajaran maupun pendidikan itu sendiri.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".