Bibliographic record
Abstract
THE GENERAL NEAR EASTERN BACKGROUND It was in the Hejazi cities of Mecca and Yathrib – later renamed Medina – that a man called Muḥammad came forward to proclaim a new religion with a political order at its center. By the time of his death in 11/632, he had left behind a small state and clear notions of justice, but with underdeveloped ideas of law and an even less developed judiciary. Soon, however, Islam was to conquer lands east and west, ranging from western China to the Iberian peninsula. Along with this territorial expansion, the new religion generated a full-fledged, sophisticated law and legal system in the short span of the three-and-a-half centuries that followed its inception. By the time of Muḥammad, Mecca and its northern neighbor Yathrib had known a long history of settlement and were largely a part of the cultural continuum that had dominated the Near East since the time of the Sumerians. True, the two cities were not direct participants in the empire cultures that prevailed elsewhere in the Near East, but they were tied to them in more ways than one. Prior to the Arab expansion in the name of Islam, Arabian society had developed the same types of institutions and forms of culture that were established in the imperial societies to the south and north, a development that would later facilitate the Arab conquest of this region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".