Bibliographic record
Abstract
In Islamic Studies, the status of Hadith comes next to the Holy Quran. The teachings of Quran are interpreted in the light of Hadith. Moreover, all commandments and queries of Shariah have come through Hadith. In the beginning of Islam, the Muslims considered the Hadith as the most important source of knowledge. In this regard, Muhaddiseen offered such services and wrote such books as ever exemplified in the history of the world. It is fact that the concoction of Hadith started in the Nabvi Era and continued till the period of followers (Tabeen). There were multi-dimensional motives behind this evil like lust, atheism, religious biasness, sectarian indifferences, fame and obedience of kings and rulers etc. Muhadditheen face this evil’s challenge with zeal and zest in every era of the Islamic history. They framed the best principles to overcome the situation for the safeguard the Hadith. In this regard, the Muhadditheen of the Sub-continent played an unforgettable and historical role. So that I have discussed the concocted the Hadith in this article. The article is so informative and enlightening that the reader feels it useful in this field. Particularly, the Sub-continent has always played a vital role in this regard, for the most authentic books of Hadith have come from this part of the world.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.979 | 0.982 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".