The Legal Objectives Related to the Hadiths Described as Half, One-Third, or A Quarter of Islam
Bibliographic record
Abstract
The research aims to collect hadiths that scholars have determined to be half, one-third, or a quarter of Islam. Knowing the reasons for this and the resulting objectives. I used the inductive analytical approach. I concluded that what is meant are hadiths that have great impact, many benefits, and many rulings based on them. With the diversity of its descriptions it's clear the confinement in twelve hadiths without repetition, it came to achieve two great goals, which are worshiping Allah, and unity and gathering. The goal of worshiping Allah is achieved through His command, to human to purify his outward and inward parts and perform the obligatory duties and pillars; And forbidding him from committing forbidden things and sins, and argue him to sincerity and good intentions, he blocked the pretexts and paths that lead to breaching him, guide him to what achieves the perfection of this goal, by abandoning what does not concern him, and asceticism. As for achieving the goal of unity and gathering, it is through his command to love goodness for Muslims, speaking good and don't speak evilly, Honoring neighbors and guests; He forbids the permissibility of Muslim blood and anger, and guides him to ways to resolve conflict when it occurs by establishing a litigation mechanism and a judicial system. Finally, I recommend that researchers study the Sunnah as an objective study, and I suggest studying the hadiths that scholars have described as one-fifth of Islam, or under its orbit.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".