Bibliographic record
Abstract
Quran is the primary source for recognition of religious teachings, jurisprudence decrees and social and personal relationships and at the same time, it is the perfect source. Quran is definite in terms of issuance and fundamentalists considered authority for the Quranic verses. Major part of Quranic verses indicate principles of Islam doctrine, for example verses about the God, hereafter, heaven and hell, reward and punishment indicate beliefs. Another part of verses express the ethical aspect and relationships among Muslims. For example, verses related to the forgiveness, avoiding lie, gossip and hypocrisy and the importance of piety are Quranic ethical verses. Third part of the verses which are less than two other parts are verses related to the decrees. Nisa is one of juridical Sura in the Quran and it has numerous juridical decrees in itself. It is the fourth Sura of Quran. In fact, the major theme of this Sura is legislation. It seems that one point that should be considered in the inference and extraction of decree verses is the abrogating and abrogated knowledge of verses and whether they revealed in the Mecca or Medina. Otherwise, abrogated verses may use as the juridical decree for inference and a false decree issues. This research seeks to study the juridical decrees of Nisa Sura.
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 | 0.002 | 0.003 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".