Bibliographic record
Abstract
Abstract: The leadership transition after Prophet Muhammad seems to be attractive which is seen from political legal administration. This article examines the period of Umar bin Khattab’s leadership as the second caliph of Islamic history. Many legacies of national policy in his period which had changed the political aspects of Islamic administration and had been the main resources for heads of state up to now. Responsive legal politics became one of the superiorities of others. Using normative legal research, this article used second sources taken from many scientific sources. Afterwards the author explains many sources with historical approach to get systematic knowledge.Keywords: Legal Politics, Umar bin Khattab, Political TransitionAbstrak: Transisi kepemimpinan pasca Nabi Muhammad SAW memiliki daya tarik dari sisi politik hukum pemerintahan. Tulisan ini mengkaji masa kepemimpinan Umar bin Khattab sebagai khalifah kedua dalam sejarah Islam. Banyak warisan kebijakan negara di masanya yang telah mengubah aspek-aspek politik hukum pemerintahan Islam dan telah menjadi referensi utama bagi pemimpin negara hingga saat ini. Politik hukum responsif menjadi salah satu keunggulan dari banyak keunggulan lainnya. Dengan metode penelitian hukum normatif, tulisan ini menggunakan data sekunder yang diambil dari beberapa literatur ilmiah. Kemudian penulis menguraikan bebagai sumber data dengan pendekatan sejarah untuk menghasilkan kajian yang sistematis.Keywords: politik hukum, Umar bin Khattab, transisi politik
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".