Bibliographic record
Abstract
Islam is a complete and comprehensive code of life. Islam has far-reaching and result-oriented instructions to deal with our fellow Muslims in the most upright and beautiful manners. Human beings can seek guidance that enables them to deal/treat the non-Muslims in an excellent way on the basis of justice and equality. Dr. Muhammad Sa'eed Ramadan Al-Buti was a great scholar of his time. He wrote numerous books on different subjects with valuable ideas and perspectives. In this article, his analytical study about Islamic etiquettes of dealing with the Non-Muslims will be presented. Dr. Al-Buti viewed friendship and overly-compassionate behavior towards the Non-Muslim to be Haram (Impermissible) as numerous Holy Verses of the Glorious Quran that indicate on the impermissibility of establishing friendship with the Non-Muslims. However, the impermissibility of being fraternizing with them does not necessitate the impermissibility about them to be treated with tolerance and forbearance. In fact, justice highlights that they should be treated with kindness and soft speech. Neither does it necessitate that the Non-Muslims should be oppressed nor does emphasizes that they should be deemed in society as despicable people and to be treated in such a way. The Holy Prophet Muhammad's (ﷺ) blessed autobiography teaches the humanity to treat them without any discrimination. They must be treated politely and graciously with tolerance and good etiquettes.
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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 | 0.002 |
| 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 | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.978 | 0.976 |
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".